{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 740,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys, os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "params = {\"ytick.color\" : \"w\",\n",
    "          \"xtick.color\" : \"w\",\n",
    "          \"axes.labelcolor\" : \"w\",\n",
    "          \"axes.edgecolor\" : \"w\"}\n",
    "plt.rcParams.update(params)\n",
    "\n",
    "\n",
    "sys.path.append('../')\n",
    "import xai\n",
    "from xai.xdata import XData\n",
    "from importlib import reload\n",
    "reload(xai)\n",
    "reload(xai.xdata)\n",
    "import xai\n",
    "from xai.xdata import XData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 741,
   "metadata": {},
   "outputs": [],
   "source": [
    "csv_path = 'data/adult.data'\n",
    "csv_columns = [\"age\", \"workclass\", \"fnlwgt\", \"education\", \"education-num\", \"marital-status\",\n",
    "                   \"occupation\", \"relationship\", \"ethnicity\", \"gender\", \"capital-gain\", \"capital-loss\",\n",
    "                   \"hours-per-week\", \"native-country\", \"loan\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 742,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(csv_path, names=csv_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 743,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "xd = XData(\"loan\", df)\n",
    "xd.set_protected([\"gender\", \"ethnicity\", \"native-country\", \"age\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 744,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>loan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass  fnlwgt   education  education-num  \\\n",
       "0   39          State-gov   77516   Bachelors             13   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "2   38            Private  215646     HS-grad              9   \n",
       "3   53            Private  234721        11th              7   \n",
       "4   28            Private  338409   Bachelors             13   \n",
       "\n",
       "        marital-status          occupation    relationship ethnicity   gender  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family     White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband     White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family     White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband     Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife     Black   Female   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week  native-country    loan  \n",
       "0          2174             0              40   United-States   <=50K  \n",
       "1             0             0              13   United-States   <=50K  \n",
       "2             0             0              40   United-States   <=50K  \n",
       "3             0             0              40   United-States   <=50K  \n",
       "4             0             0              40            Cuba   <=50K  "
      ]
     },
     "execution_count": 744,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xd.df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 716,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ims = xd.show_imbalances(cross=[])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 717,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xd.set_threshold(0.8)\n",
    "im = xd.show_imbalance(\"gender\", cross=[])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 723,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEoCAYAAACzVD1FAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAHP1JREFUeJzt3Xt0VNXB9/FvuChYL0HFFgzLYkEugRLutKCAyEVQwAUo9CIqb6lUStGXLniqFpfo0/pqrcVrvaNVo2Ip2KI8GEQFEUFRFBBBQYmwFIMgPMglmPePczJM2AlECJnAfD9rnTVn9pxzZgcm+Z199j57MoqKipAkKVm1VFdAklT1GA6SpIDhIEkKGA6SpIDhIEkKGA6SpIDhIEkKGA6SpIDhIEkKGA6SpECNVFfgEDjvhyR9d18CdQ+0kS0HSUovn5RnI8NBkhQwHCRJAcNBkhQ4kjukA7t37yY/P58dO3akuirSYVOrVi2ysrKoWbNmqquio9hRFQ75+fmccMIJ/PCHPyQjIyPV1ZEqXFFREQUFBeTn59OwYcNUV0dHsaPqstKOHTs45ZRTDAYdtTIyMjjllFNsHeuwO6rCATAYdNTzM67KcNSFQyoVFBSQk5NDTk4OP/jBDzj99NPJyckhMzOT5s2bV/j7zZ07lwsuuOA77dOtWzcWL14clD/66KOMHj36gPv36dOHzMzM4H3PPvvsxM9ev359Bg4cWOL1RYsWUb16daZOnQrAJ598Qtu2bcnJySE7O5v77rsPgO3bt9OvXz+aNm1KdnY2EyZMKLMuf/rTn2jUqBFNmjRh1qxZifIXX3yRJk2a0KhRI/785z+Xuf/YsWN59dVXAcjLy6NNmzbk5OTQpUsXVq9eDcDOnTu55JJLaNSoER07dmTt2rWlHqus91yzZg0dO3akcePGXHLJJezatQuAO++8kxYtWtC3b99E2bx587jmmmsS+27cuJE+ffqUWX/pcDqq+hxS7ZRTTuGdd94B4IYbbuD4449n3LhxrF27tlx/xAsLC6lRo2r/l/z+979n+/bt/P3vfy9R/tprryXWBw0axIABAxLP9+zZw/jx4+ndu3eirF69erz++usce+yxbNu2jRYtWtC/f38yMzMZN24c3bt3Z9euXfTo0YMXXniB888/v8T7LV++nNzcXJYtW8b69es577zz+PDDDwG46qqrmD17NllZWbRv357+/fsH4bxp0ybeeOMN7rjjDgBGjRrF9OnTadasGffccw833XQTjz76KA899BB16tRh9erV5ObmMn78eJ5++ukSx9qzZ0+Z7zl+/Hiuvvpqhg4dypVXXslDDz3EqFGjePDBB1m6dCnXX389s2bN4oILLmDSpEnk5uYmjlu3bl3q1avH/Pnz6dy588H8d+1Xm473VPgx09nbC3+T6ipUKFsOlWTPnj386le/Ijs7m169evHNN98A0Zn8H/7wB7p27crf/vY3Nm7cyKBBg2jfvj3t27dn/vz5ALzyyiuJM/PWrVuzdetWALZt28bgwYNp2rQpP//5zykqimYVycvLo3Xr1rRs2ZIrrriCnTt3BnV65JFHOOuss+jatWvifQ6kR48enHDCCWW+vnXrVubMmVOi5XDnnXcyaNAgTjvttETZMcccw7HHHgtEZ+fffvstAMcddxzdu3dPbNOmTRvy8/OD95k+fTpDhw7l2GOPpWHDhjRq1Ig333yTN998k0aNGnHmmWdyzDHHMHToUKZPnx7sP3Xq1BJn5RkZGXz99dcAbNmyhfr16yfeZ/jw4QAMHjyYvLy8xL9xsbLes6ioiDlz5jB48GAAhg8fzr/+9a/Efrt372b79u3UrFmTxx9/nL59+1KnTp0Sxx44cCBPPPFEmf/e0uFStU9TD8XYsRCfxVeYnByIzzS/q1WrVvHUU0/xwAMPcPHFF/Pcc8/xi1/8AoDNmzfzyiuvAPCzn/2Mq6++mi5duvDpp5/Su3dvVqxYwW233cbdd99N586d2bZtG7Vq1QJgyZIlLFu2jPr169O5c2fmz59Pu3btuOyyy8jLy+Oss87i0ksv5d5772Xs2LGJ+mzYsIGJEyfy1ltvcdJJJ9G9e3dat24NwIwZM1i8eDE33njjd/45p02bRo8ePTjxxBMB+Oyzz5g2bRpz5sxh0aJFJbZdt24d/fr1Y/Xq1dx6662JP8jFNm/ezPPPP8/vfve74H0+++wzOnXqlHielZXFZ599BkCDBg1KlC9cuDDYf/78+Yk/2gAPPvggffv2pXbt2px44om88cYbifcpPl6NGjU46aSTKCgo4NRTTy1Rl9Les6CggMzMzERrMLmO48aNo1OnTmRnZ9O5c2cGDhzIiy++GNSzXbt2XHfddUG5dLjZcqgkDRs2JCcnB4C2bduWuHZ9ySWXJNZfeuklRo8eTU5ODv379+frr79m69atdO7cmWuuuYbJkyezefPmxB+cDh06kJWVRbVq1cjJyWHt2rWsXLmShg0bctZZZwHRGWvxtfViCxcupFu3btStW5djjjmmRB369+9/UMEA8NRTTzFs2LDE87Fjx3LLLbdQvXr1YNsGDRqwdOlSVq9ezZQpU/j8888TrxUWFjJs2DDGjBnDmWeeGey779k7RGf/ZZXva8OGDdStu3fusb/+9a/MnDmT/Px8Lr/88sS1//Ic72Dq8stf/pIlS5bwj3/8g9tvv50xY8bwwgsvMHjwYK6++upES+q0005j/fr1wXGkw+3obTkc5Bn+4VJ8CQWgevXqictKAN/73vcS699++y0LFiygdu3aJfafMGEC/fr1Y+bMmXTq1ImXXnqp1OMWFhaW+kepNBU96qWgoIA333yTadOmJcoWL17M0KFDAfjyyy+ZOXMmNWrUKHHZqX79+mRnZ/Paa68lzuZHjhxJ48aNS7R2kmVlZbFu3brE8/z8/ETLo6zyZLVr104MB924cSPvvvsuHTt2BKKwLr7kVPw+WVlZFBYWsmXLFk4++eRy1eXUU09l8+bNib6k0uqyfv16Fi1axMSJE+nQoQMLFizg2muvJS8vj549e7Jjx47gsyBVBlsOVUyvXr246667Es+LO7g/+ugjWrZsyfjx42nXrh0ffPBBmcdo2rQpa9euTYy4efzxx+natWuJbTp27MjcuXMpKChg9+7dPPvss4dc92effZYLLrggcckLotE6a9euZe3atQwePJh77rmHgQMHkp+fnwjIr776ivnz59OkSRMArrvuOrZs2ZLoLC5N//79yc3NZefOnaxZs4ZVq1bRoUMH2rdvz6pVq1izZg27du0iNzeX/v37B/s3a9Ys8e9Tp04dtmzZkujQnj17Ns2aNUu8z5QpU4Con+Lcc88NQrWs98zIyKB79+6JEVpTpkwp0VEPcP311zNp0iQAvvnmGzIyMqhWrRrbt28H4MMPP6RFixbl+eeXKpThUMVMnjyZxYsX8+Mf/5jmzZsnhnjecccdtGjRglatWlG7du1g9E6yWrVq8cgjjzBkyBBatmxJtWrVuPLKK0tsU69ePW644QZ+8pOfcN5559GmTZvEazNmzOCPf/xjqcc+++yzGTJkCHl5eWRlZZUYQpqbm1viktL+rFixgo4dO9KqVSu6du3KuHHjaNmyJfn5+dx8880sX748MbT0wQcfDOqVnZ3NxRdfTPPmzenTpw9333031atXp0aNGtx111307t2bZs2acfHFF5OdnR28f79+/Zg7dy4Q9SU88MADDBo0iFatWvH4449z6623AjBixAgKCgpo1KgRt99+e2KY6vr16+nbt29i/7Le85ZbbuH222+nUaNGFBQUMGLEiEQdlixZApDo6xkxYgQtW7bk7bffTrRcXn75Zfr161euf1OpImWU9xJEFRRUfMWKFYkzPulAunTpwr///W8yMzNTXZUynXPOOUyfPj0YxVQRn3WHslasI2go61tAuwNtZMtBaesvf/kLn376aaqrUaaNGzdyzTXXBMEgVYajt0NaOoDiDuiqqm7dusGd5lJlseUgSQoYDpKkgOEgSQqUJxwaAC8DK4BlQPFcBicDs4FV8WNxr1kGMBlYDSwF2iQda3i8/ap4vVhb4L14n8nxMSRJKVKecCgE/i/QDOgEXAU0ByYAeUDj+LF4buXz47LGwEjg3rj8ZGAi0BHoEK8XB8q98bbF+zlPcZLLLrsscSOVJFWG8oxW2hAvAFuJWhCnAwOAbnH5FGAuMD4uf4zoPoQ3gEygXrztbGBTvM9sohCYC5wILIjLHwMGAi8cxM9TQkWP4z5SxjEfCVN/S6ravmufww+B1sBC4PvsDY0NQPF8zKcD65L2yY/L9leeX0r5EWnSpEk0bdqUnj17MmzYMG677TY++ugj+vTpQ9u2bTn77LMTU19cdtlljBkzhp/+9KeceeaZidZBUVERo0ePpnnz5vTr148vvvgicfy33nqLrl270rZtW3r37s2GDdF/wb5Tf0vSofgup5fHA88BY4Gv97Ndaf0FRQdRXpqR8VIlLV68mOeee44lS5ZQWFhImzZtaNu2LSNHjuS+++6jcePGLFy4kN/85jfMmTMHiGYHnTdvHh988AH9+/dn8ODBTJs2jZUrV/Lee+/x+eef07x5c6644gp2797Nb3/7W6ZPn07dunV5+umnufbaa3n44YeBklN/S9KhKG841CQKhieAf8ZlnxNdLtoQPxaf3uYTdWIXywLWx+Xd9imfG5dnlbJ9ae6PFyg7QFJm3rx5DBgwIDGL5oUXXsiOHTt4/fXXGTJkSGK75C/eGThwINWqVaN58+aJKatfffVVhg0bRvXq1alfvz7nnnsuACtXruT999+nZ8+eQPQFQvXq1UscK3nabUk6FOUJhwzgIaK+htuTymcQjTj6c/w4Pal8NJBL1Pm8hShAZgH/zd5O6F7AfxH1QWwl6uxeCFwK3HmwP1AqlTZP1bfffktmZmZidtV9JU+5nbx/adNpFxUVkZ2dzYIFC4LXoOTU35J0KMrT59AZ+CVwLvBOvPQlCoWeRMNSe8bPAWYCHxMNS30AKO7F3QRMAhbFy43s7ZweBTwY7/MRFdAZnQpdunTh+eefZ8eOHWzbto3//Oc/HHfccTRs2DAxJXZRURHvvvvufo9zzjnnkJuby549e9iwYQMvv/wyAE2aNGHjxo2JcNi9ezfLli07vD+UpLRUnpbDPMq+76BHKWVFRMNdS/NwvOxrMXDET1pf/MXyrVq14owzzqBdu3acdNJJPPHEE4waNYqbbrqJ3bt3M3ToUFq1alXmcS666CLmzJlDy5YtE9/xDNF3Kk+dOpUxY8awZcsWCgsLGTt2bKlTUkvSoXDK7gq2bds2jj/+eLZv384555zD/fffX+K7EqSK4JTdVc+RMtSdck7Z7WD4CjZy5EiWL1/Ojh07GD58uMEg6YhkOFSwJ598MtVVkKRD5sR7kqTAURcOR3AfilQufsZVGY6qcKhVqxYFBQX+8uioVVRUREFBAbVq1Up1VXSUO6r6HLKyssjPz2fjxo2prop02NSqVYusrKwDbygdgqMqHGrWrEnDhg1TXQ1JOuIdVZeVJEkVw3CQJAUMB0lSwHCQJAUMB0lSwHCQJAUMB0lSwHCQJAUMB0lSwHCQJAWO3OkzVq6EX/861bWQjlj3r1if6iocXbo9k+oalM/cueXa7MgNhyPE4rf9Bawo7drUT3UVpLRxVH2HdFXk9/RWnCPoO3qPCH42K9YR9Pks13dI2+cgSQoYDpKkgOEgSQoYDpKkgOEgSQoYDpKkgOEgSQoYDpKkgOEgSQoYDpKkgOEgSQoYDpKkgOEgSQoYDpKkgOEgSQoYDpKkgOEgSQoYDpKkQHnC4WHgC+D9pLIbgM+Ad+Klb9Jr/wWsBlYCvZPK+8Rlq4EJSeUNgYXAKuBp4Jjv8gNIkipeecLhUaI/7Pv6K5ATLzPjsubAUCA73uceoHq83A2cH28zLH4EuCU+VmPgK2DEd/8xJEkVqTzh8CqwqZzHGwDkAjuBNUSthA7xshr4GNgVbzMAyADOBabG+08BBpbzvSRJh8mh9DmMBpYSXXaqE5edDqxL2iY/Liur/BRgM1C4T3lZRgKL40WSdJgcbDjcC/yI6JLSBuAvcXlGKdsWHUR5We4H2sWLJOkwqXGQ+32etP4A8O94PR9okPRaFrA+Xi+t/EsgM65H4T7bS5JS5GBbDvWS1i9i70imGUQd0scSjUJqDLwJLIrXGxKNRhoab1sEvAwMjvcfDkw/yDpJkipIeVoOTwHdgFOJWgYT4+c5RH/c1wK/jrddBjwDLCdqCVwF7IlfGw3MIhq59HC8LcB4og7qm4AlwEMH/dNIkipEecJhWCll+/sDfnO87Gsme4e8JvuYaDSTJKmK8A5pSVLAcJAkBQwHSVLAcJAkBQwHSVLAcJAkBQwHSVLAcJAkBQwHSVLAcJAkBQwHSVLAcJAkBQwHSVLAcJAkBQwHSVLAcJAkBQwHSVLAcJAkBQwHSVLAcJAkBQwHSVLAcJAkBQwHSVLAcJAkBQwHSVLAcJAkBQwHSVLAcJAkBQwHSVLAcJAkBQwHSVLAcJAkBQwHSVLAcJAkBQwHSVLAcJAkBQwHSVLAcJAkBcoTDg8DXwDvJ5WdDMwGVsWPdeLyDGAysBpYCrRJ2md4vP2qeL1YW+C9eJ/J8TEkSSlUnnB4FOizT9kEIA9oHD9OiMvPj8saAyOBe+Pyk4GJQEegQ7xeHCj3xtsW77fve0mSKll5wuFVYNM+ZQOAKfH6FGBgUvljQBHwBpAJ1AN6E7UwNgFfxet94tdOBBbE+zyWdCxJUoocbJ/D94EN8foG4LR4/XRgXdJ2+XHZ/srzSymXJKVQjQo+Xmn9BUUHUV6WkfEiSTqMDrbl8DnRJSHixy/i9XygQdJ2WcD6A5RnlVJelvuBdvEiSTpMDjYcZrB3xNFwYHpS+aVELYJOwBaiy06zgF5EndB14vVZ8Wtb420z4n2LjyVJSpHyXFZ6CugGnEp0pj8R+DPwDDAC+BQYEm87E+hLNCx1O3B5XL4JmAQsip/fyN5O7lFEI6JqAy/EiyQphcoTDsPKKO9RSlkRcFUZ2z8cL/taDLQoRz0kSZXEO6QlSQHDQZIUMBwkSQHDQZIUMBwkSQHDQZIUMBwkSQHDQZIUMBwkSQHDQZIUMBwkSQHDQZIUMBwkSQHDQZIUMBwkSQHDQZIUMBwkSQHDQZIUMBwkSQHDQZIUMBwkSQHDQZIUMBwkSQHDQZIUMBwkSQHDQZIUMBwkSQHDQZIUMBwkSQHDQZIUMBwkSQHDQZIUMBwkSQHDQZIUMBwkSQHDQZIUMBwkSQHDQZIUMBwkSYFDDYe1wHvAO8DiuOxkYDawKn6sE5dnAJOB1cBSoE3ScYbH26+K1yVJKVQRLYfuQA7QLn4+AcgDGsePE+Ly8+OyxsBI4N64/GRgItAR6BCvFweKJCkFDsdlpQHAlHh9CjAwqfwxoAh4A8gE6gG9iVoYm4Cv4vU+h6FekqRyOtRwKAL+B3iLqDUA8H1gQ7y+ATgtXj8dWJe0b35cVla5JClFahzi/p2B9UQBMBv4YD/bZpRSVrSf8tKMZG8ISZIOk0NtOayPH78AphH1GXxOdLmI+PGLeD0faJC0b1a8f1nlpbmfqG+jXRmvS5IqwKGEw/eAE5LWewHvAzPYO+JoODA9Xp8BXErUUugEbCG67DQr3rdOvPSKyyRJKXIol5W+T9RaKD7Ok8CLwCLgGWAE8CkwJN5mJtCXaCjrduDyuHwTMCneD+DGuEySlCKHEg4fA61KKS8AepRSXgRcVcaxHo4XSVIV4B3SkqSA4SBJChgOkqSA4SBJChgOkqSA4SBJChgOkqSA4SBJChgOkqSA4SBJChgOkqSA4SBJChgOkqSA4SBJChgOkqSA4SBJChgOkqSA4SBJChgOkqSA4SBJChgOkqSA4SBJChgOkqSA4SBJChgOkqSA4SBJChgOkqSA4SBJChgOkqSA4SBJChgOkqSA4SBJChgOkqSA4SBJChgOkqSA4SBJChgOkqSA4SBJClSlcOgDrARWAxNSXBdJSmtVJRyqA3cD5wPNgWHxoyQpBapKOHQgajF8DOwCcoEBKa2RJKWxqhIOpwPrkp7nx2WSpBSokeoKxDJKKSsqpWxkvABsI+qjqNLeXvibVFehPE4Fvkx1JVS5jpDPJvj5rGhnlGejqhIO+UCDpOdZwPpStrs/XlSxFgPtUl0JqQx+PlOgqlxWWgQ0BhoCxwBDgRkprZEkpbGq0nIoBEYDs4hGLj0MLEtpjSQpjVWVcACYGS+qfF6qU1Xm5zMFMoqKSuv3lSSls6rS5yBJqkIMB0lSwHBIX7WBJqmuhFSGM4Dz4vXawAkprEtaMhzS04XAO8CL8fMcHDqsquNXwFTg7/HzLOBfqatOejIc0tMNRPNZbY6fvwP8MFWVkfZxFdAZ+Dp+vgo4LXXVSU+GQ3oqBLakuhJSGXYSTcBZrAalT6ejw8hwSE/vAz8juuGwMXAn8HpKayTt9QrwB6K+hp7As8DzKa1RGvI+h/R0HHAt0Ito0sNZwCRgRyorJcWqASMo+fl8EFsPlcpwkCQFqtL0GTr8nmf/Z1/9K6siUineY/+fzx9XVkVkyyHddD3A669USi2k0h3oewY+qZRaCDAcJEmlcLRSempMdJPRcqLv7S5epKqgE9F3vGwjGtK6h733PKiSGA7p6RHgXqL7HboDjwGPp7RG0l53AcOIbn6rDfwfouHWqkSGQ3qqDeQRDRP8hOiO6XNTWSFpH6uJ7sPZQ3Qy0z211Uk/jlZKTzuITgxWEX0D32c4PYGqju1EXxf8DvD/gA3A91JaozRkh3R6ag+sADKJbn47ieiX8I1UVkqKnQF8AdQErib6fN5D1JpQJTEcJEkBLyulp3ZE02ecQcnPgDcZKZWWHuB1P5+VyHBIT08Avye6I/XbFNdFKvYt0R3STxLdzf9NaquT3ryslJ7mAV1SXQmpFE2JhrFeSHQfzpPA/xANu1YlMhzSUw+iX8A8ornzi/0zNdWRSnUJcDdwC3BriuuSdryslJ4uJzpDq8ney0pFGA5KvdOBocBFwFdEo5WmpbRGacqWQ3p6D2iZ6kpI+3gFOAF4hmh6l037vL7vcx1GhkN6egD4K9E1XamqWMveKbuT/zBlxM/PrOwKpTPDIT2tAH4ErCHqcyj+5XOooCTAcEhXZc2b73z5kgAn3ktXnwANiCbb+4RoLhs/C5ISbDmkp4lEd0k3Ac4C6gPPAp1TWSlJVYdni+npIqLvi/7f+Pl6olEikgQYDulqF1EHdHGz0emQJZVgOKSnZ4C/E03Z/SvgJaLhrZIE2OeQznoCvYiGsc4CZqe2OpKqEsMhvXTCL/SRVA5eVkov9yStL0hZLSRVeYZDeslIWq+VslpIqvKclTW9VAPqxI/F68mB4cRmkgD7HNLNWqIpujNKec2JzSQlGA6SpIB9DpKkgOEgSQoYDpKkgOEgSQoYDpKkgOEgSQoYDlLleBQYnOpKSOVlOEhVk7MXKKUMByl0PfAB0TTmTwHjgB8BLwJvAa8BTeNtHwUmA68DH7O3dZAB3AUsB/4DnJZ0/LbAK/GxZgH14vK5wH/Hr/2uon8o6bvw7EQqqR0wCGhN9PvxNtEf8fuBK4FVQEeiGW7PjfepB3QhCowZwFSir2JtArQEvk8UEg8DNYE7gQHARuAS4GbgivhYmUDXw/jzSeViOEgldQGmA9/Ez58nmsH2p8CzSdsdm7T+L6I5q5YTBQHAOUStjj1E39E9Jy5vArRg75crVQc2JB3r6Yr4IaRDZThIJZU2KWE1YDOQU8Y+O8vYv7SJyzKAZcBPyjjW/x6oglJlsM9BKmkecCFRa+F4oB+wHVgDDIm3yQBaHeA4rwJDiVoG9YDucflKoC57w6EmkF1BdZcqjOEglbSIqN/gXeCfwGJgC/BzYERcvoyoz2B/phH1T7wH3EvUyQywi6jT+pb4WO8QXbKSqhSn7JZCxwPbgOOIWgAjiTqmpbRhn4MUuh9oTnRpaQoGg9KQLQdJUsA+B0lSwHCQJAUMB0lSwHCQJAUMB0lSwHCQJAX+PysKmqPHGViTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xd.balance(\"gender\", cross=[])\n",
    "im = xd.show_imbalance(\"gender\", cross=[])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 724,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fvBpoaB9tgCn2uTowDnPLth9z+/ZC4Ad7zDBgLbAISAEWh/OPEYl2H374Ib179w5k0fzd737HsWPH+Oijj+jXr1/guF9//TWw3adPH8qVK0eTJk3Yv38/AKtWrWLAgAGUL1+eOnXqcOWVVwLw+eefs3Xr1kCG0pMnT1K7du3AtYJTf4sUpCjBYRVQP0T5k8A9wIKgst6YIOLHfNhXBWoDnYF3gYP2uHcxQWAFcDawxpa/BPRBwUHiVKhcZqdOnaJq1apsymdVuuCU28Hnh0qn7ff7ueiii1izZo3jNcid+lukIOGOOfQCvgE+zVNeF/g6aD/dlhVUnh6iPD/DgDT7EIk5l112GW+99RbHjh3jyJEjvPPOO5x++ukkJSUF1kH2+/18+mneP63cOnXqxJw5czh58iQZGRksX74cgAsuuIADBw4EgsOJEyfYtm1byf6jJC6FM5X1dOA+oFuI10KNF/jDKM/P8/ZR2HEiRVLaU09btWpFr169aNasGeeddx4tW7akSpUqzJ49m1tvvZWHH36YEydO0L9//wLXTrj22mt5//33adq0KY0aNQqsxVCxYkXmz5/PiBEjOHz4MFlZWYwaNSpkSmqRgoQTHH4LJJHTakgENgCtMd/8g9f0SwT22fLOecpX2PLEEMeLxK277rqLBx54gKNHj9KpUyfuvPNOkpKSWLJkiePYmTNn5to/cuQIYLqUJk2aFPL6ycnJrFq1ylGevTaBSFGE0620BaiFGYeoj/mAbw58ixlkHoxpEbQFDgMZwFJMS6OafXSzZRnAT/ZYnz03eAxDJO4MGzaM5ORkmjdvzvXXX59rER2RaFGUlsOrmG/952ACwThgej7HLgJ6YKalHgVutOUHgfHAOrv/EDmD07diZkRVxgxEazBa4lqoJSlFok1RgsOAQl6vH7TtB4bnc9wM+8grDXB/AVSREPx+P36/P+RMH5F4Eukqn7pDWsqU9G+Ocvz4kYj/cESimd/vJzMzk0qVKoV9DSXekzJlxqyd3DQEEuuejs/nw0em11USKRGVKlUiMTGx8APzoeAgZcpPR7L41+TPAvteZVEViXbqVhIREQcFBxERcVBwEBERBwUHERFxUHAQEREHBQcREXFQcBAREQcFBxERcVBwEBERBwUHERFxUHAQEREHX8xmp/z8cz9//rOrl0zbEP2L0LVsXsfrKhRJLLyXEDvvp4hrVqxYD7Qs7DC1HERExCF2Ww5mYSFXNW/zrNuXdF2sZBGNhfcSYuf9FHGRay2HGcB3wNagsseAHcBm4E2gatBr92KWCf0c6B5UnmLLdgFjg8qTgI+BncBcoGIR6iQiIiWoKMFhJuaDPdi7mKU9LwG+wAQEgCZAf+Aie86zQHn7mAxcbY8ZYJ8BJgBPAg2BH4ChYf1LRETENUUJDquAg3nK/gNk2e21QPZyQ72BOcCvwG5MK6G1fewCvgKO22N6Az7gSmC+PX8W0CeMf4eIiLjIjQHpm4DFdrsu8HXQa+m2LL/yGsAhcgJNdnl+hgFp9iEiIiUk0uBwH+aDfbbd94U4xh9GeX6exwykFDqYIiIi4YtkDekhwDVAF3I+0NOBekHHJALZE95DlX+PGcxOwASZ4ONFRMQj4bYcUoAxQC/gaFD5QsyA9GmYWUgNgU+AdXY7CTMbqb891g8sB/ra84cAC8Ksk4iIuKQoweFVYA1wAaZlMBSYBJyFmbW0CXjOHrsNmAdsB5YAw4GTmFbB7cBS4DN7zDZ7zhjgDsyAdQ1geoT/JhERiVBRupUGhCgr6AP8H/aR1yL7yOsrzGwmERGJEkqfISIiDgoOIiLioOAgIiIOCg4iIuKg4CAiIg4KDiIi4qDgICIiDgoOIiLioOAgIiIOCg4iIuKg4CAiIg4KDiIi4qDgICIiDgoOIiLioOAgIiIOCg4iIuKg4CAiIg4KDiIi4lCU4DAD+A7YGlRWHbN+9E77XM2W+4CnMetBbwaaB50zxB6/025nawFssec8ba8hIiIeKkpwmAmk5CkbCywDGtrnsbb8alvWEBgGTLHl1YFxQBvMetHjyAkoU+yx2efl/VkiIlLKihIcVgEH85T1BmbZ7VlAn6DylwA/sBaoCtQGumNaGAeBH+x2in3tbGCNPeeloGuJiIhHEsI871wgw25nALXsdl3g66Dj0m1ZQeXpIcrzM8w+RESkBIUbHPITarzAH0Z5fp63j8KOExGRCIQ7W2k/pksI+/yd3U4H6gUdlwjsK6Q8MUS5iIh4KNzgsJCcGUdDgAVB5YMxLYK2wGFMt9NSoBtmELqa3V5qX/vJHuuz52ZfS0REPFKUbqVXgc7AOZhv+uOAR4F5wFBgL9DPHrsI6IGZlnoUuNGWHwTGA+vs/kPkDHLfipkRVRlYbB8iIuKhogSHAfmUdwlR5geG53P8DPvIKw24uAj1EBGRUqI7pEVExEHBQUREHBQcRETEQcFBREQcFBxERMRBwUFERBwUHERExEHBQUREHBQcRETEQcFBREQcFBxERMRBwUFERBwUHERExEHBQUREHBQcRETEQcFBREQcFBxERMRBwUFERBwiDQ6jgW3AVsxa05WAJOBjYCcwF6hojz3N7u+yr9cPus69tvxzoHuEdRIRkQhFEhzqAiOAlpg1oMsD/YEJwJNAQ+AHYKg9fqjdb2Bfn2DLm9jzLgJSgGfttURExCORthwSgMr2+XQgA7gSmG9fnwX0sdu97T729S6Az5bPAX4FdmNaEK0jrJeIiEQgkuDwDTAR2IsJCoeB9cAhIMsek45pYWCfv7bbWfb4GnnK856T1zAgzT5ERKSERBIcqmG+9ScBdYAzgKtDHOe3z758XsuvPJTnMd1YLYtVUxERKZZIgsNVmG6gA8AJ4A2gPVAV080EkAjss9vpQD27nQBUAQ7mKc97joiIeCCS4LAXaIsZa/BhxhC2A8uBvvaYIcACu73Q7mNffx/TQliIGZA+DdMKaQh8EkG9REQkQgmFH5KvjzEDyxswYwgbMd0+72AGmB+2ZdPt8dOBVMyA80FMQAAzFXYeJrBkAcOBkxHUS0REIhRJcAAYZx/BviL0bKNjQL98rvMP+xARkSigO6RFRMRBwUFERBwUHERExEHBQUREHBQcRETEQcFBREQcFBxERMRBwUFERBwUHERExEHBQUREHBQcRETEIdLcSiIiMefE1P6FHxQFKvx5jmc/Wy0HERFxUHAQEREHBQcREXFQcBAREQcFBxERcYg0OFTFLBW6A/gMaAdUB94FdtrnavZYH/A0ZpnQzUDzoOsMscfvJGedaRER8UikweFfwBLgQqAZJkCMBZYBDe3zWHvs1basITAMmGLLq2OWGm2DWV50HDkBRUREPBBJcDgb6ARMt/vHgUNAb2CWLZsF9LHbvYGXAD+wFtPqqA10x7QwDgI/2O2UCOolIiIRiiQ4nA8cAF4ENgLTgDOAc4EMe0wGUMtu1wW+Djo/3ZblVx7KMCDNPkREpIREEhwSMOMGU4BLgZ/J6UIKxReizF9AeSjPAy3tQ0RESkgkwSHdPj62+/MxwWI/prsI+/xd0PH1gs5PBPYVUC4iIh6JJDh8i+kOusDudwG2AwvJmXE0BFhgtxcCgzEthbbAYUy301KgG2YQuprdXhpBvUREJEKRJt77CzAbqAh8BdyICTjzgKHAXqCfPXYR0AMzlfWoPRbMQPR4YJ3df8iWiYiIRyINDpsI3f/fJUSZHxiez3Vm2IeIiEQB3SEtIiIOCg4iIuKg4CAiIg4KDiIi4qDgICIiDgoOIiLioOAgIiIOCg4iIuKg4CAiIg4KDiIi4qDgICIiDgoOIiLioOAgIiIOCg4iIuKg4CAiIg4KDiIi4qDgICIiDgoOIiLi4EZwKA9sBN62+0nAx8BOYC5mfWmA0+z+Lvt6/aBr3GvLPwe6u1AnERGJgBvBYSTwWdD+BOBJoCHwAzDUlg+1+w3s6xNseROgP3ARkAI8iwk4IiLikUiDQyLQE5hm933AlcB8uz8L6GO3e9t97Otd7PG9gTnAr8BuTAuidYT1EhGRCEQaHJ4C7gFO2f0awCEgy+6nA3Xtdl3ga7udBRy2xweX5z0nr2FAmn2IiEgJiSQ4XAN8B6wPKvOFOM5fyGsFnZPX80BL+xARkRKSEMG5HYBeQA+gEnA2piVR1V43C9PttM8enw7Us88JQBXgYFB5tuBzRETEA5G0HO7FfJDXxwwovw8MBJYDfe0xQ4AFdnuh3ce+/j6mhbDQnn8aZqZTQ+CTCOolIiIRiqTlkJ8xmAHmhzFTXKfb8ulAKmbA+SAmIABsA+YB2zGtjeHAyRKol4iIFJFbwWGFfQB8RejZRseAfvmc/w/7EBGRKKA7pEVExEHBQUREHBQcRETEQcFBREQcFBxERMRBwUFERBwUHERExEHBQUREHBQcRETEQcFBREQcFBxERMRBwUFERBwUHERExEHBQUREHBQcRETEQcFBREQcFBxERMRBwUFERBwiCQ71gOXAZ5h1oEfa8urAu8BO+1zNlvuApzFrSG8Gmgdda4g9fqfdFhERD0USHLKAO4HGQFtgONAEGAssAxra57H2+KttWUNgGDDFllcHxgFtMGtPjyMnoIiIiAciCQ4ZwAa7/ROmBVEX6A3MsuWzgD52uzfwEuAH1gJVgdpAd0wL4yDwg91OiaBeIiISoQSXrlMfuBT4GDgXEziwz7Xsdl3g66Bz0m1ZfuWhDLMPEREpQW4EhzOB14FRwI8FHOcLUeYvoDyU5+2joGNE4s66hhd6XYUiabVzh9dVEJdEOlupAiYwzAbesGX7Md1F2Ofv7HY6ZhA7WyKwr4ByERHxSCTBwQdMx4w1PBFUvpCcGUdDgAVB5YPteW2Bw5hup6VAN8wgdDW7vTSCeomISIQi6VbqAAwCtgCbbNlfgUeBecBQYC/Qz762COiBmcp6FLjRlh8ExgPr7P5DtkxERDwSSXD4kNDjBQBdQpT5MdNdQ5lhHyIiEgV0h7SIiDgoOIiIiIOCg4iIOCg4iIiIg4KDiIg4KDiIiIiDgoOIiDgoOIiIiIOCg4iIOCg4iIiIg4KDiIg4KDiIiIiDgoOIiDgoOIiIiIOCg4iIOCg4iIiIg4KDiIg4RFNwSAE+xywjOtbjuoiIlGnREhzKA5OBq4EmwAD7LCIiHoiW4NAa02L4CjgOzAF6e1ojEZEyzOf3+72uA0BfTLfSzXZ/ENAGuD3PccPsA+ACTDdUtDsH+N7rSsQJvZfu0vvprlh5P88DahZ2UEIpVKQofCHKQkWt5+0jlqQBLb2uRJzQe+kuvZ/uiqv3M1q6ldKBekH7icA+j+oiIlLmRUtwWAc0BJKAikB/YKGnNRIRKcOipVspCzO+sBQzc2kGsM3TGrkn1rrBopneS3fp/XRXXL2f0TIgLSIiUSRaupVERCSKKDiIiIiDgoOIiDhEy4C0iJS8WkAHoA7wC7AVMzf/lJeVkuikAWl3JWKm4XYk9x/gO8Bi9EdYXO2A/8W8n7XJ/X6+DBz2rmox5QpMMsvqwEbgO6AS0Aj4LTAfeBz40asKxqiWOP/W3wMOelkptyg4uOdFoC7wNubbWPAf4BVAC8wf6CqvKhhjFmNuhFxA6Pfzd8AT6H6YongMeAbYG+K1BOAazBTy10uzUjHsBmAEsBtYT+7fzQ6YIHE/od/vmKHg4J6LMb8U+akI/AaTYFAKV5Q8NbGSy0biy3DMvVi/5PN6MlADWFZqNSoBCg4lozImEMRCYsBYcB7mDvr3MO9tAvCTpzWKTecC/8S0cFMwafHbAdO9rJREJ81Wcl8vYBOwxO4no66PSPwJ0yc+1e4nAv/2rjoxbSYmC0Ftu/8FMMqz2sS+RpjWQXaPwSXA37yrjrsUHNw3DrM+xSG7vwmo71ltYt9wTD9u9mDpTsysGym+c4B55EyMyAJOeledmPcCcC9wwu5vxkxIiQsKDu7LQrNo3PQrZgGobAmETucuhfsZ0xee/f61Rb+rkTgd+CRPWZYXFSkJus/BfVuBP2JmfzTEzGr4yNMaxbaVwF8xYw1dgduAtzytUey6A9PF+VtgNWbBl76e1ii2fY95L7ODbV8gw7vquEsD0u47HbgP6IZZxGgpMB445mWlYlg5YCi538+RFj8tAAAN4ElEQVRpqPUQrgTMKoo+zISJEwUfLgU4H5OJtT3wA2Zq6/8Cezysk2sUHETi33WFvP5GqdQifp2B+RITVzPo1K3knrco+Ntsr9KqSJzYQsHv5yWlVZE48LsCXvOj4FBcdxTy+hOlUosSpuDgnoleVyDOXON1BeLIjV5XIM6c5XUFSoO6lUTKlp7ARZh0D9ke8qguEsXUcnBfQ+ARzN2nwX+A53tTnZjXFpMXqDEmBUl5zJTMs72sVIx6DjNh4grMoH5fnFMxpegqYSZL5A22N3lTHXfpPgf3vQhMwcx3vgJ4CUj1tEaxbRIwAHPzW2XgZkywkOJrDwzGzKx5EJM6o56nNYptqcD/AN0xU64TiaNBaQUH91XG3FLvA/4LPABc6WWF4sAuTIvhJCb4XuFtdWJWdqK4o5g00yeAJO+qE/MaYLKv/gzMwnTZNfW0Ri5St5L7jmGC7k7gduAblO4hEkcx3UmbgP/D3GR0hqc1il1vA1UxKbw3YGYqTfO0RrEt+x6RQ5iszN8SR6lyNCDtvlbAZ5g/wvFAFcyH2lovKxXDzsPky68AjMa8n8+i1OeROg3TT670GeG7GbMGxiWYFu2ZwN8xYzsxT8FBpOwoj+n6qE/uXoO4mJcv7lK3kvtaYtJnnEfu91c3bRXP5kJe1/tZfG9huj23oCVrI6Gb4CQss4G70R9gpE5h+sRfwXyo5bfqlhRdIgqqbpiIGQNbjMka7PO2OiVDwcF9B9DiPm5IBi7ETGN9Bdhun/9DHKVFLmWLMQkM/+N1RWJcc8y6DT0xa0i/ipmhGFd99BpzcF8XzAfaMsy3imzKXxOZPwCTgQmY2TZSfNcCL2Nm053AfOP1oxsKI9Ee8/d+FTCGOPpiqJaD+27EfOOtQE63kpKbhacu5hvatZgbt0YDb3pao9j2OObGt8KSGkrR1AQuxdzbkI6ZVRc3FBzc14w4uhHGQysxCc7mATcAB215RaB60L4U3U7MYlQKDJG5EdOSrYRZ3/z3xFlgAHUrlYQXgCcxfeQSvj3kfIgF/5Jmd4UoV1XxzcS8b9kDqdniYnZNKTqFaX3ttft5P0TjIj2/Wg7uuwwYglkVKnsmgx/NEimu+l5XIA7tto+K9iHhKRPpW9RycN95+ZT/t1RrIZK/MzD5gETypcR77vsvJtPllXb7KHqfJTq0w3R3fmb3m2FSkYg46EPLfeMwU9rutfsVMNMHRbz2FCa9dKbd/xTo5F11JJopOLjvWsyAVHazfR9lZFlBiQlf59k/6UktJOppQNp9xzED0NmDOUovLdHia8xNW37MgPQIcrqYRHJRy8F984CpmJTdfwLew0xvFfHaLcBwzM2F6ZgUJcM9rZFELc1WKhldMTlsfMBS4F1vqyMiUjwKDu5pixb0KQ3vYfICTcasbCaF+w/mywqYiRKPeFiXeHYbZrD/deIgOaS6ldwTPCVwjWe1iH+Dgb+R//0k4lQzaLufZ7WIfz7MTbBxkUdNwcE9wTndK3lWi/jRNZ/yfZhcNpNLsS6xTt0D7qqST/lk4CWUPkPyKAdUs8/Z28EBQ4niimcyJgvrO0Fl5YAZwP94UqPYdT4mlbQvaDtYXHyYlaJlmC8vP+Qp7wZMx9wEG/MUHNxTBbPwR3ZA2BD0mhLFFV83YAlwGqaZnp0B8zDwOw/rFYt6B21P9KwW8WMqsBwTIA7Ysj8C/8AsABQXNCAt0SwRM9vrGWAQ8DGFr98rUhoGAfdgvsT8ATNNOAWTTTguKDhItGpun2tj+nHfBf4v6PUNjjNESlc/zBeXvcDV5KQliQsKDhKtlhfwmh+T2FDEC9kr6fkws+YOYNLlxFV6fgUHEZHiKWwadVyk51dwkGh2IWYwtS7mG9k+YAGww8tKiVjnkvt3c7+31XGXgoNEqzHAAGAOJg8QmAHq/rbsUY/qJZIMPIeZofiNLUsEDmHuko6L8TAFB4lWXwAXYVJlBKsIbAMalnqNRIxNwJ8xs+eCtcVMc21W6jUqAbpDWqLVKaBOiPLa9jWJ3G2YaZi636l4zsAZGMDkVoubFP36pSh5ShQXnlGYO1F3krNAzW+ABsDtXlUqzmTnAhqI7pIujsWYO/dfIud3sx4m79cSryrlNnUrlbw6mG+7bVE+oOIqB7TGDPr5MGMP69DqZcVVBXNneSitMO+pFM/V5EyWyP7dXAgs8rJSblJwcE9X8l+3YQJmgFUiUx3lqApHGmUgF5C4S2MO7pmMM69KOWAmcTJAVcr+FrTdBDNAvR6TnqCNFxWKYdm5gIJTd//RlsdNLqBSFHyTWwXM7+pC4J/A6Z7UqAQoOLinG/A4cJ3dr4T5hamAEsWF47qg7ceAkUASJl33k57UKHa9gPndfB/TxTkK+DtwBbDZw3rFqplB249ixsEeBypjprjGBQ1Iu2cPcBUmUVwtlCjOTXUwg4AAn2D+CKV4UoFjwEZMLqAOxFkuoFIUnIq/C2bc5gSwCvjUkxqVAAUH92QniruHnERxLweVx8WNMaUoeA2CRExz/ah9rYJXlYpRwbmATgdqYLqZ4ioXUCmqAlyL6Xk5jZx7cfzE0cJKCg7ueTxoezPm1vrsMiWKK77eefazu0DPBaaUcl1i3TVeVyDOrCRn6u9azO/kfswiVN97VSm3abaSSNkR17mAxF1qObhLieJKxj2YtRyyn6V4ykQuIA+1xPQWHPe6Im7SbCX3jMEkhPNhBk3X2e05wFgP6xUP+ud5luKZiZnt1RgzaeIqzBeZUcCL3lUrLtQGPsLMoosr6lZyjxLFlZwNmIH9jcClHtclFu0k/9+/XZipmBKescBvMe9vZ2+r4i51K7knO1Fc3oU+lChOvFYmcgF5ZBBwOWZm3W+BL72tjnsUHNyjRHESrUYQOhfQZOIoF5AHrsCMJ36P6Z4bCvzV0xq5SN1K7lKiuJKhbiWJRi8Dr2AC7NmY9C4XECc9BWo5uOsUZt5ztuooMLhhhX1e7mUlYtgl5KTJqICZPNEa2Ao8TM7NhVJ0VTGZlgfZ/R8xf/s9iJPU/Go5uOdvmD80MIni/o35Q/RhFlQJtTiISGnIbnmBuTGzBqYbpI/dHuxRvSSKKTi4J/gP8B1gEmYgsDXwFNDeo3rFstMxs0CC89X8BtMa+ybkGRJKcHfcJnJyAfkw763SZ4iD7nMoGUoU544TwBvkXnpxGmYGmBRddi6g64njXEDiLo05uEeJ4tx3AngT0y03A9NqqIlZvEaKrkzkAhJ3qVvJPZfn2V8PHMH8IfZFS4SG60LMegQdMeM6PwJPe1ojKeuys9lGekxUU3CQWPABZg75m8BlOJe7lOKJy1xApWgF8Domb9reoPKKmN/PIZiZdTNLu2Ju0phDybgnz7NEZjpmrGEzCgyRittcQKUoBTMp4lVMcs3twFeYG2AHYFYqnOlV5dyilkPJyJ65FDyDScJ3OpCBGVB9z+O6xLq4zQXkkQrAOcAvmCy3cUMD0iXLV/ghUgRHMTNuJHJxmwvIIycwX1zijrqVRMqOULmAREJScBApO4Zixm8A5gL90GeA5EO/GCJlQ3YuoOybM4NzAYk4aMyhZKywz0oUF74yMZe8FB3CuajPoFAHioBmK0n0WkEZmEsuEq0UHNylRHHuqQTcBAwEkjDffCsB5YH/YO443+RZ7UTinIKDuypgZoNcAvxsy/6DWR1K+YDCF7dzyUWilQak3RWcKA6UKM4t2XPJFRjCU5T7bXRPjuSi4OC+acCNdnswZj65iJeWA3/BfFkJVhG4EpiFGcMRCdBsJfftsM+NMHlWLvOwLiJgcgHdhMkFFGr85kk0fiN5aMyhZNyA+WP8BhMgRKKFxm+kSBQcSoYSxYlITFNwEBERBw1Ii4iIg4KDiIg4KDiIiIiDgoN7dKORiMQNBQf36EYjEYkbmq3kHiWKE5G4oeBQMnSjkYjENAUHERFx0JiDiIg4KDiIiIiDgoPEuyNeVyBM9YGtXldCyi4FB5H4UN7rCkh8UXCQssIHPIb5Nr6FnNX6zgSWARtseW9bXh/4DHgB2IaZjlw5xHV/C6wF1gEPkbulcrct3ww8WITrtsCsP74GGB50nfK27tnX+rMt74y5v+YVW3cR1yg4SFlxHZAMNAOuwnzY1gaOAdcCzYErgMfJuZO9Ieb+lIswU5KvD3Hdf9lHK2BfUHk3e35r+3NbAJ0Kue6LwAigXZ6fMRQ4bH9GK+BPmHtpsNe/D2hShPdApMgUHKSsuAyzEtpJYD+wEvNB6wP+iflG/h5QFzjXnrObnBsX12O+9efVDnjNbr8SVN7NPjZiWiUXYoJCftetAlS19QJIzXOtwfacj4EaQdf6xF5PxFVaJlTKivzyWg0EamK+2Z8A9mDubAf4Nei4k4TuViro5z0CTM1TXj+f6/qA/G468mFSsyzNU94Z+LkYdRIpMrUcpKxYhRlnKI8JBp0w37qrAN9hAsMVwHlFuNbt9gFmvCG7W6h/0DFLMelUzrT7dYFaBVzzEKbrKHvN8YF5rnUr5s57MOuTn1GEeoqETS0HKSvexHQBfYr5hn4P8C0wG3gLSMN02+wowrUuBFbb7VHAy8CdwDuYD3gwA82NMYPLYAaq/xfTUsjPjcAM4Ci5WwnTMC2ODZhWxAGgTxHqKRI2pc8QKb63MQPcxzHrhf+CCTj9gQHkzHgSiVkKDiKR6QhMwnyjP4TpStrlaY1EXKDgICIiDhqQFhERBwUHERFxUHAQEREHBQcREXFQcBAREYf/B0SguYZsXbetAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "im = xd.show_imbalance(\"gender\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 729,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xd.reset()\n",
    "xd.balance(\"gender\")\n",
    "im = xd.show_imbalance(\"gender\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 649,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education-num</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.072772</td>\n",
       "      <td>0.063541</td>\n",
       "      <td>0.086781</td>\n",
       "      <td>0.059029</td>\n",
       "      <td>0.052904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fnlwgt</th>\n",
       "      <td>-0.072772</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.035251</td>\n",
       "      <td>-0.004080</td>\n",
       "      <td>-0.017931</td>\n",
       "      <td>-0.009535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education-num</th>\n",
       "      <td>0.063541</td>\n",
       "      <td>-0.035251</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.130791</td>\n",
       "      <td>0.101153</td>\n",
       "      <td>0.161564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>capital-gain</th>\n",
       "      <td>0.086781</td>\n",
       "      <td>-0.004080</td>\n",
       "      <td>0.130791</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.049321</td>\n",
       "      <td>0.058159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>capital-loss</th>\n",
       "      <td>0.059029</td>\n",
       "      <td>-0.017931</td>\n",
       "      <td>0.101153</td>\n",
       "      <td>-0.049321</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.048870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hours-per-week</th>\n",
       "      <td>0.052904</td>\n",
       "      <td>-0.009535</td>\n",
       "      <td>0.161564</td>\n",
       "      <td>0.058159</td>\n",
       "      <td>0.048870</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     age    fnlwgt  education-num  capital-gain  capital-loss  \\\n",
       "age             1.000000 -0.072772       0.063541      0.086781      0.059029   \n",
       "fnlwgt         -0.072772  1.000000      -0.035251     -0.004080     -0.017931   \n",
       "education-num   0.063541 -0.035251       1.000000      0.130791      0.101153   \n",
       "capital-gain    0.086781 -0.004080       0.130791      1.000000     -0.049321   \n",
       "capital-loss    0.059029 -0.017931       0.101153     -0.049321      1.000000   \n",
       "hours-per-week  0.052904 -0.009535       0.161564      0.058159      0.048870   \n",
       "\n",
       "                hours-per-week  \n",
       "age                   0.052904  \n",
       "fnlwgt               -0.009535  \n",
       "education-num         0.161564  \n",
       "capital-gain          0.058159  \n",
       "capital-loss          0.048870  \n",
       "hours-per-week        1.000000  "
      ]
     },
     "execution_count": 649,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xd.correlations()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 650,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/scipy/stats/stats.py:245: RuntimeWarning: The input array could not be properly checked for nan values. nan values will be ignored.\n",
      "  \"values. nan values will be ignored.\", RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[ 1.00000000e+00,  3.24786623e-02, -7.38703298e-02,\n",
       "         1.21617065e-02,  8.73482849e-02, -3.11351026e-01,\n",
       "        -4.85247198e-03, -1.88576384e-01,  3.65196235e-02,\n",
       "         4.76615219e-02,  1.30183769e-01,  6.06924724e-02,\n",
       "         9.78145377e-02,  1.40791861e-02,  2.93211372e-01],\n",
       "       [ 3.24786623e-02,  1.00000000e+00, -1.92585118e-02,\n",
       "         8.63415421e-03,  1.07035890e-02, -4.65716193e-02,\n",
       "         1.90236946e-01, -9.53165841e-02,  6.36757529e-02,\n",
       "         1.06434527e-01,  1.52747483e-02,  2.18582053e-02,\n",
       "         9.87006957e-02, -1.23886581e-02,  2.32039960e-02],\n",
       "       [-7.38703298e-02, -1.92585118e-02,  1.00000000e+00,\n",
       "        -5.18384762e-03, -2.92269331e-02,  2.94727112e-02,\n",
       "         3.28861465e-03, -1.31226554e-02, -3.02782792e-02,\n",
       "         3.03834439e-02, -7.84037104e-03, -1.42408206e-02,\n",
       "        -9.63250458e-03, -5.09296229e-02, -2.23244848e-02],\n",
       "       [ 1.21617065e-02,  8.63415421e-03, -5.18384762e-03,\n",
       "         1.00000000e+00,  1.03051186e-01, -1.77072579e-02,\n",
       "        -4.01730769e-02,  1.59006395e-03,  2.84726500e-02,\n",
       "        -1.87502490e-02,  8.01761994e-03,  4.06631432e-03,\n",
       "         7.08969908e-03,  7.18118878e-02,  3.07246577e-02],\n",
       "       [ 8.73482849e-02,  1.07035890e-02, -2.92269331e-02,\n",
       "         1.03051186e-01,  1.00000000e+00, -7.61031653e-02,\n",
       "         1.52249284e-01,  4.91006137e-04,  4.80111126e-02,\n",
       "        -8.18041553e-02,  1.60632554e-01,  9.50606511e-02,\n",
       "         1.87903417e-01,  3.74974862e-02,  3.98025752e-01],\n",
       "       [-3.11351026e-01, -4.65716193e-02,  2.94727112e-02,\n",
       "        -1.77072579e-02, -7.61031653e-02,  1.00000000e+00,\n",
       "         3.40568045e-03,  1.12898032e-01, -6.53722575e-02,\n",
       "        -6.72981537e-02, -5.91513295e-02, -3.37956485e-02,\n",
       "        -1.59098690e-01, -3.20564303e-02, -2.63297252e-01],\n",
       "       [-4.85247198e-03,  1.90236946e-01,  3.28861465e-03,\n",
       "        -4.01730769e-02,  1.52249284e-01,  3.40568045e-03,\n",
       "         1.00000000e+00, -8.70739383e-02,  1.28885400e-02,\n",
       "         7.92739582e-02,  2.76043928e-02,  3.06246897e-02,\n",
       "         1.01808961e-01, -4.29705455e-03,  7.67582871e-02],\n",
       "       [-1.88576384e-01, -9.53165841e-02, -1.31226554e-02,\n",
       "         1.59006395e-03,  4.91006137e-04,  1.12898032e-01,\n",
       "        -8.70739383e-02,  1.00000000e+00, -9.83490393e-02,\n",
       "        -7.25356496e-01, -1.11814423e-02, -1.33364540e-02,\n",
       "        -2.75714505e-01, -1.74447237e-02,  1.59774662e-03],\n",
       "       [ 3.65196235e-02,  6.36757529e-02, -3.02782792e-02,\n",
       "         2.84726500e-02,  4.80111126e-02, -6.53722575e-02,\n",
       "         1.28885400e-02, -9.83490393e-02,  1.00000000e+00,\n",
       "         7.91388305e-02,  2.30537753e-02,  3.25413902e-02,\n",
       "         6.29687418e-02,  1.90549833e-01,  8.62685699e-02],\n",
       "       [ 4.76615219e-02,  1.06434527e-01,  3.03834439e-02,\n",
       "        -1.87502490e-02, -8.18041553e-02, -6.72981537e-02,\n",
       "         7.92739582e-02, -7.25356496e-01,  7.91388305e-02,\n",
       "         1.00000000e+00, -2.37523496e-02,  7.29406169e-03,\n",
       "         2.28405704e-01, -4.83889749e-03, -5.55637165e-02],\n",
       "       [ 1.30183769e-01,  1.52747483e-02, -7.84037104e-03,\n",
       "         8.01761994e-03,  1.60632554e-01, -5.91513295e-02,\n",
       "         2.76043928e-02, -1.11814423e-02,  2.30537753e-02,\n",
       "        -2.37523496e-02,  1.00000000e+00, -9.59246772e-02,\n",
       "         7.82250747e-02,  1.86964130e-02,  2.82302835e-01],\n",
       "       [ 6.06924724e-02,  2.18582053e-02, -1.42408206e-02,\n",
       "         4.06631432e-03,  9.50606511e-02, -3.37956485e-02,\n",
       "         3.06246897e-02, -1.33364540e-02,  3.25413902e-02,\n",
       "         7.29406169e-03, -9.59246772e-02,  1.00000000e+00,\n",
       "         4.82951660e-02,  2.25115258e-02,  1.33392125e-01],\n",
       "       [ 9.78145377e-02,  9.87006957e-02, -9.63250458e-03,\n",
       "         7.08969908e-03,  1.87903417e-01, -1.59098690e-01,\n",
       "         1.01808961e-01, -2.75714505e-01,  6.29687418e-02,\n",
       "         2.28405704e-01,  7.82250747e-02,  4.82951660e-02,\n",
       "         1.00000000e+00,  1.33435861e-02,  2.28725843e-01],\n",
       "       [ 1.40791861e-02, -1.23886581e-02, -5.09296229e-02,\n",
       "         7.18118878e-02,  3.74974862e-02, -3.20564303e-02,\n",
       "        -4.29705455e-03, -1.74447237e-02,  1.90549833e-01,\n",
       "        -4.83889749e-03,  1.86964130e-02,  2.25115258e-02,\n",
       "         1.33435861e-02,  1.00000000e+00,  3.29010952e-02],\n",
       "       [ 2.93211372e-01,  2.32039960e-02, -2.23244848e-02,\n",
       "         3.07246577e-02,  3.98025752e-01, -2.63297252e-01,\n",
       "         7.67582871e-02,  1.59774662e-03,  8.62685699e-02,\n",
       "        -5.55637165e-02,  2.82302835e-01,  1.33392125e-01,\n",
       "         2.28725843e-01,  3.29010952e-02,  1.00000000e+00]])"
      ]
     },
     "execution_count": 650,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xd.correlations(include_categorical=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 651,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = xd.correlations(include_categorical=True, plot_type=\"matrix\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 652,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>loan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>255693</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>45</td>\n",
       "      <td>4</td>\n",
       "      <td>32896</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53</td>\n",
       "      <td>4</td>\n",
       "      <td>213378</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>389147</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>4</td>\n",
       "      <td>190942</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>116489</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>99891</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>249351</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>163665</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>39</td>\n",
       "      <td>7</td>\n",
       "      <td>85874</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>48</td>\n",
       "      <td>4</td>\n",
       "      <td>80651</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>49</td>\n",
       "      <td>4</td>\n",
       "      <td>125421</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>214896</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>328923</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>75024</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>48</td>\n",
       "      <td>4</td>\n",
       "      <td>26490</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "      <td>51111</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>62</td>\n",
       "      <td>2</td>\n",
       "      <td>291175</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>49</td>\n",
       "      <td>4</td>\n",
       "      <td>136455</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>187119</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1980</td>\n",
       "      <td>65</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>66</td>\n",
       "      <td>2</td>\n",
       "      <td>189834</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>95566</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>187983</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>31606</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>167787</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>63</td>\n",
       "      <td>4</td>\n",
       "      <td>154526</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>301199</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>54</td>\n",
       "      <td>2</td>\n",
       "      <td>166398</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>190293</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>196857</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51404</th>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
       "      <td>167728</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51405</th>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>209900</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51406</th>\n",
       "      <td>61</td>\n",
       "      <td>4</td>\n",
       "      <td>716416</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51407</th>\n",
       "      <td>44</td>\n",
       "      <td>4</td>\n",
       "      <td>235786</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7298</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51408</th>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>227332</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51409</th>\n",
       "      <td>52</td>\n",
       "      <td>5</td>\n",
       "      <td>254211</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51410</th>\n",
       "      <td>41</td>\n",
       "      <td>5</td>\n",
       "      <td>177905</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51411</th>\n",
       "      <td>46</td>\n",
       "      <td>6</td>\n",
       "      <td>342907</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51412</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>223206</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51413</th>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>119545</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51414</th>\n",
       "      <td>54</td>\n",
       "      <td>4</td>\n",
       "      <td>205337</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51415</th>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>249392</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51416</th>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "      <td>342448</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51417</th>\n",
       "      <td>61</td>\n",
       "      <td>6</td>\n",
       "      <td>244087</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51418</th>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "      <td>372525</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51419</th>\n",
       "      <td>45</td>\n",
       "      <td>5</td>\n",
       "      <td>145290</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51420</th>\n",
       "      <td>55</td>\n",
       "      <td>6</td>\n",
       "      <td>141807</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51421</th>\n",
       "      <td>31</td>\n",
       "      <td>6</td>\n",
       "      <td>348038</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1902</td>\n",
       "      <td>50</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51422</th>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>157028</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51423</th>\n",
       "      <td>77</td>\n",
       "      <td>5</td>\n",
       "      <td>84979</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>20051</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51424</th>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>118853</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4386</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51425</th>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>94477</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7298</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51426</th>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>313986</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51427</th>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>179668</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51428</th>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>145160</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51429</th>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>320451</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51430</th>\n",
       "      <td>59</td>\n",
       "      <td>4</td>\n",
       "      <td>31137</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51431</th>\n",
       "      <td>47</td>\n",
       "      <td>4</td>\n",
       "      <td>387468</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51432</th>\n",
       "      <td>33</td>\n",
       "      <td>4</td>\n",
       "      <td>155343</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3103</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51433</th>\n",
       "      <td>53</td>\n",
       "      <td>4</td>\n",
       "      <td>278114</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>51434 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  workclass  fnlwgt  education  education-num  marital-status  \\\n",
       "0       34          4  255693         11              9               2   \n",
       "1       45          4   32896          4              3               4   \n",
       "2       53          4  213378         11              9               5   \n",
       "3       18          7  389147         11              9               4   \n",
       "4       57          4  190942          3              2               6   \n",
       "5       21          4  116489         15             10               4   \n",
       "6       20          0   99891         15             10               4   \n",
       "7       24          4  249351         11              9               2   \n",
       "8       24          4  163665         11              9               4   \n",
       "9       39          7   85874         11              9               0   \n",
       "10      48          4   80651         11              9               0   \n",
       "11      49          4  125421         11              9               0   \n",
       "12      36          0  214896          6              5               0   \n",
       "13      28          4  328923         11              9               4   \n",
       "14      38          0   75024          5              4               0   \n",
       "15      48          4   26490          9             13               6   \n",
       "16      41          2   51111          9             13               6   \n",
       "17      62          2  291175          9             13               6   \n",
       "18      49          4  136455          9             13               4   \n",
       "19      35          4  187119          9             13               0   \n",
       "20      66          2  189834          5              4               6   \n",
       "21      28          4   95566          3              2               3   \n",
       "22      36          0  187983         11              9               4   \n",
       "23      21          4   31606         15             10               4   \n",
       "24      22          4  167787         11              9               4   \n",
       "25      63          4  154526         15             10               6   \n",
       "26      21          4  301199         15             10               4   \n",
       "27      54          2  166398         15             10               0   \n",
       "28      24          4  190293          9             13               4   \n",
       "29      19          4  196857         11              9               4   \n",
       "...    ...        ...     ...        ...            ...             ...   \n",
       "51404   39          4  167728          9             13               2   \n",
       "51405   32          2  209900          9             13               2   \n",
       "51406   61          4  716416         15             10               2   \n",
       "51407   44          4  235786         15             10               2   \n",
       "51408   60          2  227332         12             14               2   \n",
       "51409   52          5  254211         12             14               2   \n",
       "51410   41          5  177905          9             13               2   \n",
       "51411   46          6  342907         11              9               2   \n",
       "51412   51          1  223206         10             16               2   \n",
       "51413   28          4  119545         15             10               2   \n",
       "51414   54          4  205337          9             13               4   \n",
       "51415   37          2  249392          8             11               2   \n",
       "51416   38          4  342448         15             10               2   \n",
       "51417   61          6  244087         11              9               2   \n",
       "51418   39          5  372525         12             14               2   \n",
       "51419   45          5  145290         11              9               2   \n",
       "51420   55          6  141807          9             13               2   \n",
       "51421   31          6  348038          9             13               2   \n",
       "51422   26          4  157028         12             14               4   \n",
       "51423   77          5   84979         10             16               2   \n",
       "51424   43          2  118853         15             10               2   \n",
       "51425   26          4   94477         15             10               2   \n",
       "51426   90          0  313986         11              9               2   \n",
       "51427   35          4  179668         15             10               2   \n",
       "51428   40          4  145160         10             16               2   \n",
       "51429   35          4  320451         12             14               2   \n",
       "51430   59          4   31137         11              9               2   \n",
       "51431   47          4  387468         15             10               2   \n",
       "51432   33          4  155343         11              9               2   \n",
       "51433   53          4  278114          5              4               2   \n",
       "\n",
       "       occupation  relationship  ethnicity  gender  capital-gain  \\\n",
       "0               1             5          4       0             0   \n",
       "1               7             3          4       0             0   \n",
       "2              12             1          4       0             0   \n",
       "3              12             1          2       0             0   \n",
       "4               9             1          2       0             0   \n",
       "5              12             3          4       0             0   \n",
       "6               0             3          4       0             0   \n",
       "7               7             5          4       0             0   \n",
       "8               8             1          4       0          2174   \n",
       "9               1             4          4       0             0   \n",
       "10              7             1          4       0             0   \n",
       "11              1             4          4       0             0   \n",
       "12              0             4          4       0             0   \n",
       "13              1             2          4       0             0   \n",
       "14              0             1          4       0             0   \n",
       "15              8             4          4       0             0   \n",
       "16              1             1          4       0             0   \n",
       "17             10             1          4       0             0   \n",
       "18             10             1          4       0             0   \n",
       "19             12             1          4       0             0   \n",
       "20              8             1          4       0             0   \n",
       "21              8             3          3       0             0   \n",
       "22              0             4          4       0             0   \n",
       "23              8             3          4       0             0   \n",
       "24              8             1          4       0             0   \n",
       "25              8             1          4       0             0   \n",
       "26              7             3          4       0             0   \n",
       "27              4             4          2       0             0   \n",
       "28             10             1          4       0             0   \n",
       "29              8             1          4       0             0   \n",
       "...           ...           ...        ...     ...           ...   \n",
       "51404           4             0          4       1             0   \n",
       "51405          10             0          4       1             0   \n",
       "51406          12             0          4       1             0   \n",
       "51407          10             0          4       1          7298   \n",
       "51408           4             0          4       1             0   \n",
       "51409          10             0          4       1         15024   \n",
       "51410           4             0          4       1          7688   \n",
       "51411          12             0          2       1             0   \n",
       "51412          10             0          1       1         15024   \n",
       "51413           4             3          4       1          7688   \n",
       "51414           4             1          4       1             0   \n",
       "51415          11             0          4       1             0   \n",
       "51416           4             0          4       1             0   \n",
       "51417           3             0          4       1             0   \n",
       "51418           4             0          4       1             0   \n",
       "51419           8             0          4       1             0   \n",
       "51420           4             0          4       1             0   \n",
       "51421          10             0          4       1             0   \n",
       "51422          10             1          4       1             0   \n",
       "51423           5             0          4       1         20051   \n",
       "51424          13             0          4       1          4386   \n",
       "51425          14             0          4       1          7298   \n",
       "51426           0             0          4       1             0   \n",
       "51427          10             0          4       1             0   \n",
       "51428          10             0          4       1             0   \n",
       "51429           4             0          1       1             0   \n",
       "51430           3             0          4       1             0   \n",
       "51431          10             0          4       1             0   \n",
       "51432          12             0          4       1          3103   \n",
       "51433           4             0          4       1             0   \n",
       "\n",
       "       capital-loss  hours-per-week  native-country  loan  \n",
       "0                 0              20              38     0  \n",
       "1                 0              35              38     0  \n",
       "2                 0              33              38     0  \n",
       "3                 0              30              38     0  \n",
       "4                 0              30              38     0  \n",
       "5                 0              60              38     0  \n",
       "6                 0              30              38     0  \n",
       "7                 0              40              38     0  \n",
       "8                 0              40              38     0  \n",
       "9                 0              40              38     0  \n",
       "10                0              55              38     0  \n",
       "11                0              38              38     0  \n",
       "12                0              40              25     0  \n",
       "13                0              38              38     0  \n",
       "14                0              40              38     0  \n",
       "15                0              20              38     0  \n",
       "16                0              40              38     0  \n",
       "17                0              48              38     0  \n",
       "18                0              45              38     0  \n",
       "19             1980              65              38     0  \n",
       "20                0              20              38     0  \n",
       "21                0              35               6     0  \n",
       "22                0              40              38     0  \n",
       "23                0              25              11     0  \n",
       "24                0              40              38     0  \n",
       "25                0              40              38     0  \n",
       "26                0              40              38     0  \n",
       "27                0              35              38     0  \n",
       "28                0              40              38     0  \n",
       "29                0              40              38     0  \n",
       "...             ...             ...             ...   ...  \n",
       "51404             0              40              38     1  \n",
       "51405             0              65              38     1  \n",
       "51406             0              44              38     1  \n",
       "51407             0              45              38     1  \n",
       "51408             0              40              38     1  \n",
       "51409             0              60              38     1  \n",
       "51410             0              70              38     1  \n",
       "51411             0              60              38     1  \n",
       "51412             0              40              39     1  \n",
       "51413             0              50              38     1  \n",
       "51414             0              50              38     1  \n",
       "51415             0              40              38     1  \n",
       "51416             0              55              38     1  \n",
       "51417             0              52              38     1  \n",
       "51418             0              60              38     1  \n",
       "51419             0              40              38     1  \n",
       "51420             0              40              38     1  \n",
       "51421          1902              50              32     1  \n",
       "51422             0              55              38     1  \n",
       "51423             0              40              38     1  \n",
       "51424             0              99              38     1  \n",
       "51425             0              55              38     1  \n",
       "51426             0              40              38     1  \n",
       "51427             0              40              38     1  \n",
       "51428             0              40              38     1  \n",
       "51429             0              65              16     1  \n",
       "51430             0              40              38     1  \n",
       "51431             0              40              33     1  \n",
       "51432             0              40              38     1  \n",
       "51433             0              40              38     1  \n",
       "\n",
       "[51434 rows x 15 columns]"
      ]
     },
     "execution_count": 652,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xd.convert_categories()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 653,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>loan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.458735</td>\n",
       "      <td>4</td>\n",
       "      <td>0.646592</td>\n",
       "      <td>11</td>\n",
       "      <td>-0.612832</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-1.715406</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.389245</td>\n",
       "      <td>4</td>\n",
       "      <td>-1.489994</td>\n",
       "      <td>4</td>\n",
       "      <td>-2.923964</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-0.482613</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.005958</td>\n",
       "      <td>4</td>\n",
       "      <td>0.240798</td>\n",
       "      <td>11</td>\n",
       "      <td>-0.612832</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-0.646985</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.692161</td>\n",
       "      <td>7</td>\n",
       "      <td>1.926393</td>\n",
       "      <td>11</td>\n",
       "      <td>-0.612832</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-0.893544</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.314314</td>\n",
       "      <td>4</td>\n",
       "      <td>0.025641</td>\n",
       "      <td>3</td>\n",
       "      <td>-3.309153</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-0.893544</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.460894</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.688351</td>\n",
       "      <td>15</td>\n",
       "      <td>-0.227643</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>1.572043</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-1.537983</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.847523</td>\n",
       "      <td>15</td>\n",
       "      <td>-0.227643</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-0.893544</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1.229626</td>\n",
       "      <td>4</td>\n",
       "      <td>0.585773</td>\n",
       "      <td>11</td>\n",
       "      <td>-0.612832</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-0.071682</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-1.229626</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.235941</td>\n",
       "      <td>11</td>\n",
       "      <td>-0.612832</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.018173</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-0.071682</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.073290</td>\n",
       "      <td>7</td>\n",
       "      <td>-0.981944</td>\n",
       "      <td>11</td>\n",
       "      <td>-0.612832</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-0.071682</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.620512</td>\n",
       "      <td>4</td>\n",
       "      <td>-1.032031</td>\n",
       "      <td>11</td>\n",
       "      <td>-0.612832</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>1.161112</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.697601</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.602694</td>\n",
       "      <td>11</td>\n",
       "      <td>-0.612832</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-0.236054</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.304557</td>\n",
       "      <td>0</td>\n",
       "      <td>0.255356</td>\n",
       "      <td>6</td>\n",
       "      <td>-2.153587</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-0.071682</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.921270</td>\n",
       "      <td>4</td>\n",
       "      <td>1.348855</td>\n",
       "      <td>11</td>\n",
       "      <td>-0.612832</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-0.236054</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.150379</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.085993</td>\n",
       "      <td>5</td>\n",
       "      <td>-2.538776</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-0.071682</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.620512</td>\n",
       "      <td>4</td>\n",
       "      <td>-1.551426</td>\n",
       "      <td>9</td>\n",
       "      <td>0.927924</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-1.715406</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.080889</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.315315</td>\n",
       "      <td>9</td>\n",
       "      <td>0.927924</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-0.071682</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.699760</td>\n",
       "      <td>2</td>\n",
       "      <td>0.986858</td>\n",
       "      <td>9</td>\n",
       "      <td>0.927924</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>0.585808</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.697601</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.496880</td>\n",
       "      <td>9</td>\n",
       "      <td>0.927924</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>0.339250</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-0.381646</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.011021</td>\n",
       "      <td>9</td>\n",
       "      <td>0.927924</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>3.980931</td>\n",
       "      <td>1.982974</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.008116</td>\n",
       "      <td>2</td>\n",
       "      <td>0.015015</td>\n",
       "      <td>5</td>\n",
       "      <td>-2.538776</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-1.715406</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-0.921270</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.888999</td>\n",
       "      <td>3</td>\n",
       "      <td>-3.309153</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-0.482613</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-0.304557</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.002735</td>\n",
       "      <td>11</td>\n",
       "      <td>-0.612832</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-0.071682</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-1.460894</td>\n",
       "      <td>4</td>\n",
       "      <td>-1.502364</td>\n",
       "      <td>15</td>\n",
       "      <td>-0.227643</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-1.304475</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-1.383805</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.196412</td>\n",
       "      <td>11</td>\n",
       "      <td>-0.612832</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-0.071682</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.776849</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.323582</td>\n",
       "      <td>15</td>\n",
       "      <td>-0.227643</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-0.071682</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-1.460894</td>\n",
       "      <td>4</td>\n",
       "      <td>1.082987</td>\n",
       "      <td>15</td>\n",
       "      <td>-0.227643</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-0.071682</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1.083047</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.209732</td>\n",
       "      <td>15</td>\n",
       "      <td>-0.227643</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-0.482613</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-1.229626</td>\n",
       "      <td>4</td>\n",
       "      <td>0.019417</td>\n",
       "      <td>9</td>\n",
       "      <td>0.927924</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-0.071682</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-1.615072</td>\n",
       "      <td>4</td>\n",
       "      <td>0.082365</td>\n",
       "      <td>11</td>\n",
       "      <td>-0.612832</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-0.071682</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51404</th>\n",
       "      <td>-0.073290</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.196978</td>\n",
       "      <td>9</td>\n",
       "      <td>0.927924</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-0.071682</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51405</th>\n",
       "      <td>-0.612914</td>\n",
       "      <td>2</td>\n",
       "      <td>0.207445</td>\n",
       "      <td>9</td>\n",
       "      <td>0.927924</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>1.982974</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51406</th>\n",
       "      <td>1.622671</td>\n",
       "      <td>4</td>\n",
       "      <td>5.064847</td>\n",
       "      <td>15</td>\n",
       "      <td>-0.227643</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>0.257063</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51407</th>\n",
       "      <td>0.312156</td>\n",
       "      <td>4</td>\n",
       "      <td>0.455687</td>\n",
       "      <td>15</td>\n",
       "      <td>-0.227643</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.530175</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>0.339250</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51408</th>\n",
       "      <td>1.545582</td>\n",
       "      <td>2</td>\n",
       "      <td>0.374615</td>\n",
       "      <td>12</td>\n",
       "      <td>1.313112</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-0.071682</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51409</th>\n",
       "      <td>0.928869</td>\n",
       "      <td>5</td>\n",
       "      <td>0.632380</td>\n",
       "      <td>12</td>\n",
       "      <td>1.313112</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1.302176</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>1.572043</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51410</th>\n",
       "      <td>0.080889</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.099382</td>\n",
       "      <td>9</td>\n",
       "      <td>0.927924</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.569145</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>2.393906</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51411</th>\n",
       "      <td>0.466334</td>\n",
       "      <td>6</td>\n",
       "      <td>1.482959</td>\n",
       "      <td>11</td>\n",
       "      <td>-0.612832</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>1.572043</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51412</th>\n",
       "      <td>0.851780</td>\n",
       "      <td>1</td>\n",
       "      <td>0.335047</td>\n",
       "      <td>10</td>\n",
       "      <td>2.083490</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.302176</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-0.071682</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51413</th>\n",
       "      <td>-0.921270</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.659044</td>\n",
       "      <td>15</td>\n",
       "      <td>-0.227643</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.569145</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>0.750181</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51414</th>\n",
       "      <td>1.083047</td>\n",
       "      <td>4</td>\n",
       "      <td>0.163686</td>\n",
       "      <td>9</td>\n",
       "      <td>0.927924</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>0.750181</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51415</th>\n",
       "      <td>-0.227468</td>\n",
       "      <td>2</td>\n",
       "      <td>0.586166</td>\n",
       "      <td>8</td>\n",
       "      <td>0.157546</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-0.071682</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51416</th>\n",
       "      <td>-0.150379</td>\n",
       "      <td>4</td>\n",
       "      <td>1.478558</td>\n",
       "      <td>15</td>\n",
       "      <td>-0.227643</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>1.161112</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51417</th>\n",
       "      <td>1.622671</td>\n",
       "      <td>6</td>\n",
       "      <td>0.535292</td>\n",
       "      <td>11</td>\n",
       "      <td>-0.612832</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>0.914553</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51418</th>\n",
       "      <td>-0.073290</td>\n",
       "      <td>5</td>\n",
       "      <td>1.766991</td>\n",
       "      <td>12</td>\n",
       "      <td>1.313112</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>1.572043</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51419</th>\n",
       "      <td>0.389245</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.412154</td>\n",
       "      <td>11</td>\n",
       "      <td>-0.612832</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-0.071682</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51420</th>\n",
       "      <td>1.160136</td>\n",
       "      <td>6</td>\n",
       "      <td>-0.445556</td>\n",
       "      <td>9</td>\n",
       "      <td>0.927924</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-0.071682</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51421</th>\n",
       "      <td>-0.690003</td>\n",
       "      <td>6</td>\n",
       "      <td>1.532165</td>\n",
       "      <td>9</td>\n",
       "      <td>0.927924</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>3.814346</td>\n",
       "      <td>0.750181</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51422</th>\n",
       "      <td>-1.075448</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.299589</td>\n",
       "      <td>12</td>\n",
       "      <td>1.313112</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>1.161112</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51423</th>\n",
       "      <td>2.856097</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.990526</td>\n",
       "      <td>10</td>\n",
       "      <td>2.083490</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1.804486</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-0.071682</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51424</th>\n",
       "      <td>0.235067</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.665680</td>\n",
       "      <td>15</td>\n",
       "      <td>-0.227643</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.239201</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>4.777306</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51425</th>\n",
       "      <td>-1.075448</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.899442</td>\n",
       "      <td>15</td>\n",
       "      <td>-0.227643</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.530175</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>1.161112</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51426</th>\n",
       "      <td>3.858255</td>\n",
       "      <td>0</td>\n",
       "      <td>1.205612</td>\n",
       "      <td>11</td>\n",
       "      <td>-0.612832</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-0.071682</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51427</th>\n",
       "      <td>-0.381646</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.082475</td>\n",
       "      <td>15</td>\n",
       "      <td>-0.227643</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-0.071682</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51428</th>\n",
       "      <td>0.003799</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.413401</td>\n",
       "      <td>10</td>\n",
       "      <td>2.083490</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-0.071682</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51429</th>\n",
       "      <td>-0.381646</td>\n",
       "      <td>4</td>\n",
       "      <td>1.267610</td>\n",
       "      <td>12</td>\n",
       "      <td>1.313112</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>1.982974</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51430</th>\n",
       "      <td>1.468493</td>\n",
       "      <td>4</td>\n",
       "      <td>-1.506862</td>\n",
       "      <td>11</td>\n",
       "      <td>-0.612832</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-0.071682</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51431</th>\n",
       "      <td>0.543423</td>\n",
       "      <td>4</td>\n",
       "      <td>1.910292</td>\n",
       "      <td>15</td>\n",
       "      <td>-0.227643</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-0.071682</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51432</th>\n",
       "      <td>-0.535824</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.315748</td>\n",
       "      <td>11</td>\n",
       "      <td>-0.612832</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.111001</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-0.071682</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51433</th>\n",
       "      <td>1.005958</td>\n",
       "      <td>4</td>\n",
       "      <td>0.861606</td>\n",
       "      <td>5</td>\n",
       "      <td>-2.538776</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.199059</td>\n",
       "      <td>-0.247764</td>\n",
       "      <td>-0.071682</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>51434 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            age  workclass    fnlwgt  education  education-num  \\\n",
       "0     -0.458735          4  0.646592         11      -0.612832   \n",
       "1      0.389245          4 -1.489994          4      -2.923964   \n",
       "2      1.005958          4  0.240798         11      -0.612832   \n",
       "3     -1.692161          7  1.926393         11      -0.612832   \n",
       "4      1.314314          4  0.025641          3      -3.309153   \n",
       "5     -1.460894          4 -0.688351         15      -0.227643   \n",
       "6     -1.537983          0 -0.847523         15      -0.227643   \n",
       "7     -1.229626          4  0.585773         11      -0.612832   \n",
       "8     -1.229626          4 -0.235941         11      -0.612832   \n",
       "9     -0.073290          7 -0.981944         11      -0.612832   \n",
       "10     0.620512          4 -1.032031         11      -0.612832   \n",
       "11     0.697601          4 -0.602694         11      -0.612832   \n",
       "12    -0.304557          0  0.255356          6      -2.153587   \n",
       "13    -0.921270          4  1.348855         11      -0.612832   \n",
       "14    -0.150379          0 -1.085993          5      -2.538776   \n",
       "15     0.620512          4 -1.551426          9       0.927924   \n",
       "16     0.080889          2 -1.315315          9       0.927924   \n",
       "17     1.699760          2  0.986858          9       0.927924   \n",
       "18     0.697601          4 -0.496880          9       0.927924   \n",
       "19    -0.381646          4 -0.011021          9       0.927924   \n",
       "20     2.008116          2  0.015015          5      -2.538776   \n",
       "21    -0.921270          4 -0.888999          3      -3.309153   \n",
       "22    -0.304557          0 -0.002735         11      -0.612832   \n",
       "23    -1.460894          4 -1.502364         15      -0.227643   \n",
       "24    -1.383805          4 -0.196412         11      -0.612832   \n",
       "25     1.776849          4 -0.323582         15      -0.227643   \n",
       "26    -1.460894          4  1.082987         15      -0.227643   \n",
       "27     1.083047          2 -0.209732         15      -0.227643   \n",
       "28    -1.229626          4  0.019417          9       0.927924   \n",
       "29    -1.615072          4  0.082365         11      -0.612832   \n",
       "...         ...        ...       ...        ...            ...   \n",
       "51404 -0.073290          4 -0.196978          9       0.927924   \n",
       "51405 -0.612914          2  0.207445          9       0.927924   \n",
       "51406  1.622671          4  5.064847         15      -0.227643   \n",
       "51407  0.312156          4  0.455687         15      -0.227643   \n",
       "51408  1.545582          2  0.374615         12       1.313112   \n",
       "51409  0.928869          5  0.632380         12       1.313112   \n",
       "51410  0.080889          5 -0.099382          9       0.927924   \n",
       "51411  0.466334          6  1.482959         11      -0.612832   \n",
       "51412  0.851780          1  0.335047         10       2.083490   \n",
       "51413 -0.921270          4 -0.659044         15      -0.227643   \n",
       "51414  1.083047          4  0.163686          9       0.927924   \n",
       "51415 -0.227468          2  0.586166          8       0.157546   \n",
       "51416 -0.150379          4  1.478558         15      -0.227643   \n",
       "51417  1.622671          6  0.535292         11      -0.612832   \n",
       "51418 -0.073290          5  1.766991         12       1.313112   \n",
       "51419  0.389245          5 -0.412154         11      -0.612832   \n",
       "51420  1.160136          6 -0.445556          9       0.927924   \n",
       "51421 -0.690003          6  1.532165          9       0.927924   \n",
       "51422 -1.075448          4 -0.299589         12       1.313112   \n",
       "51423  2.856097          5 -0.990526         10       2.083490   \n",
       "51424  0.235067          2 -0.665680         15      -0.227643   \n",
       "51425 -1.075448          4 -0.899442         15      -0.227643   \n",
       "51426  3.858255          0  1.205612         11      -0.612832   \n",
       "51427 -0.381646          4 -0.082475         15      -0.227643   \n",
       "51428  0.003799          4 -0.413401         10       2.083490   \n",
       "51429 -0.381646          4  1.267610         12       1.313112   \n",
       "51430  1.468493          4 -1.506862         11      -0.612832   \n",
       "51431  0.543423          4  1.910292         15      -0.227643   \n",
       "51432 -0.535824          4 -0.315748         11      -0.612832   \n",
       "51433  1.005958          4  0.861606          5      -2.538776   \n",
       "\n",
       "       marital-status  occupation  relationship  ethnicity  gender  \\\n",
       "0                   2           1             5          4       0   \n",
       "1                   4           7             3          4       0   \n",
       "2                   5          12             1          4       0   \n",
       "3                   4          12             1          2       0   \n",
       "4                   6           9             1          2       0   \n",
       "5                   4          12             3          4       0   \n",
       "6                   4           0             3          4       0   \n",
       "7                   2           7             5          4       0   \n",
       "8                   4           8             1          4       0   \n",
       "9                   0           1             4          4       0   \n",
       "10                  0           7             1          4       0   \n",
       "11                  0           1             4          4       0   \n",
       "12                  0           0             4          4       0   \n",
       "13                  4           1             2          4       0   \n",
       "14                  0           0             1          4       0   \n",
       "15                  6           8             4          4       0   \n",
       "16                  6           1             1          4       0   \n",
       "17                  6          10             1          4       0   \n",
       "18                  4          10             1          4       0   \n",
       "19                  0          12             1          4       0   \n",
       "20                  6           8             1          4       0   \n",
       "21                  3           8             3          3       0   \n",
       "22                  4           0             4          4       0   \n",
       "23                  4           8             3          4       0   \n",
       "24                  4           8             1          4       0   \n",
       "25                  6           8             1          4       0   \n",
       "26                  4           7             3          4       0   \n",
       "27                  0           4             4          2       0   \n",
       "28                  4          10             1          4       0   \n",
       "29                  4           8             1          4       0   \n",
       "...               ...         ...           ...        ...     ...   \n",
       "51404               2           4             0          4       1   \n",
       "51405               2          10             0          4       1   \n",
       "51406               2          12             0          4       1   \n",
       "51407               2          10             0          4       1   \n",
       "51408               2           4             0          4       1   \n",
       "51409               2          10             0          4       1   \n",
       "51410               2           4             0          4       1   \n",
       "51411               2          12             0          2       1   \n",
       "51412               2          10             0          1       1   \n",
       "51413               2           4             3          4       1   \n",
       "51414               4           4             1          4       1   \n",
       "51415               2          11             0          4       1   \n",
       "51416               2           4             0          4       1   \n",
       "51417               2           3             0          4       1   \n",
       "51418               2           4             0          4       1   \n",
       "51419               2           8             0          4       1   \n",
       "51420               2           4             0          4       1   \n",
       "51421               2          10             0          4       1   \n",
       "51422               4          10             1          4       1   \n",
       "51423               2           5             0          4       1   \n",
       "51424               2          13             0          4       1   \n",
       "51425               2          14             0          4       1   \n",
       "51426               2           0             0          4       1   \n",
       "51427               2          10             0          4       1   \n",
       "51428               2          10             0          4       1   \n",
       "51429               2           4             0          1       1   \n",
       "51430               2           3             0          4       1   \n",
       "51431               2          10             0          4       1   \n",
       "51432               2          12             0          4       1   \n",
       "51433               2           4             0          4       1   \n",
       "\n",
       "       capital-gain  capital-loss  hours-per-week  native-country  loan  \n",
       "0         -0.199059     -0.247764       -1.715406              38     0  \n",
       "1         -0.199059     -0.247764       -0.482613              38     0  \n",
       "2         -0.199059     -0.247764       -0.646985              38     0  \n",
       "3         -0.199059     -0.247764       -0.893544              38     0  \n",
       "4         -0.199059     -0.247764       -0.893544              38     0  \n",
       "5         -0.199059     -0.247764        1.572043              38     0  \n",
       "6         -0.199059     -0.247764       -0.893544              38     0  \n",
       "7         -0.199059     -0.247764       -0.071682              38     0  \n",
       "8          0.018173     -0.247764       -0.071682              38     0  \n",
       "9         -0.199059     -0.247764       -0.071682              38     0  \n",
       "10        -0.199059     -0.247764        1.161112              38     0  \n",
       "11        -0.199059     -0.247764       -0.236054              38     0  \n",
       "12        -0.199059     -0.247764       -0.071682              25     0  \n",
       "13        -0.199059     -0.247764       -0.236054              38     0  \n",
       "14        -0.199059     -0.247764       -0.071682              38     0  \n",
       "15        -0.199059     -0.247764       -1.715406              38     0  \n",
       "16        -0.199059     -0.247764       -0.071682              38     0  \n",
       "17        -0.199059     -0.247764        0.585808              38     0  \n",
       "18        -0.199059     -0.247764        0.339250              38     0  \n",
       "19        -0.199059      3.980931        1.982974              38     0  \n",
       "20        -0.199059     -0.247764       -1.715406              38     0  \n",
       "21        -0.199059     -0.247764       -0.482613               6     0  \n",
       "22        -0.199059     -0.247764       -0.071682              38     0  \n",
       "23        -0.199059     -0.247764       -1.304475              11     0  \n",
       "24        -0.199059     -0.247764       -0.071682              38     0  \n",
       "25        -0.199059     -0.247764       -0.071682              38     0  \n",
       "26        -0.199059     -0.247764       -0.071682              38     0  \n",
       "27        -0.199059     -0.247764       -0.482613              38     0  \n",
       "28        -0.199059     -0.247764       -0.071682              38     0  \n",
       "29        -0.199059     -0.247764       -0.071682              38     0  \n",
       "...             ...           ...             ...             ...   ...  \n",
       "51404     -0.199059     -0.247764       -0.071682              38     1  \n",
       "51405     -0.199059     -0.247764        1.982974              38     1  \n",
       "51406     -0.199059     -0.247764        0.257063              38     1  \n",
       "51407      0.530175     -0.247764        0.339250              38     1  \n",
       "51408     -0.199059     -0.247764       -0.071682              38     1  \n",
       "51409      1.302176     -0.247764        1.572043              38     1  \n",
       "51410      0.569145     -0.247764        2.393906              38     1  \n",
       "51411     -0.199059     -0.247764        1.572043              38     1  \n",
       "51412      1.302176     -0.247764       -0.071682              39     1  \n",
       "51413      0.569145     -0.247764        0.750181              38     1  \n",
       "51414     -0.199059     -0.247764        0.750181              38     1  \n",
       "51415     -0.199059     -0.247764       -0.071682              38     1  \n",
       "51416     -0.199059     -0.247764        1.161112              38     1  \n",
       "51417     -0.199059     -0.247764        0.914553              38     1  \n",
       "51418     -0.199059     -0.247764        1.572043              38     1  \n",
       "51419     -0.199059     -0.247764       -0.071682              38     1  \n",
       "51420     -0.199059     -0.247764       -0.071682              38     1  \n",
       "51421     -0.199059      3.814346        0.750181              32     1  \n",
       "51422     -0.199059     -0.247764        1.161112              38     1  \n",
       "51423      1.804486     -0.247764       -0.071682              38     1  \n",
       "51424      0.239201     -0.247764        4.777306              38     1  \n",
       "51425      0.530175     -0.247764        1.161112              38     1  \n",
       "51426     -0.199059     -0.247764       -0.071682              38     1  \n",
       "51427     -0.199059     -0.247764       -0.071682              38     1  \n",
       "51428     -0.199059     -0.247764       -0.071682              38     1  \n",
       "51429     -0.199059     -0.247764        1.982974              16     1  \n",
       "51430     -0.199059     -0.247764       -0.071682              38     1  \n",
       "51431     -0.199059     -0.247764       -0.071682              33     1  \n",
       "51432      0.111001     -0.247764       -0.071682              38     1  \n",
       "51433     -0.199059     -0.247764       -0.071682              38     1  \n",
       "\n",
       "[51434 rows x 15 columns]"
      ]
     },
     "execution_count": 653,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xd.normalize_numeric()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiments \n",
    "Below are todos and experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 555,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>loan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>284582</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>49</td>\n",
       "      <td>Private</td>\n",
       "      <td>160187</td>\n",
       "      <td>9th</td>\n",
       "      <td>5</td>\n",
       "      <td>Married-spouse-absent</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>Jamaica</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>209642</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>31</td>\n",
       "      <td>Private</td>\n",
       "      <td>45781</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>14084</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>42</td>\n",
       "      <td>Private</td>\n",
       "      <td>159449</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>5178</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>280464</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>30</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>141297</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>India</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>23</td>\n",
       "      <td>Private</td>\n",
       "      <td>122272</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>32</td>\n",
       "      <td>Private</td>\n",
       "      <td>205019</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>121772</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>?</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>34</td>\n",
       "      <td>Private</td>\n",
       "      <td>245487</td>\n",
       "      <td>7th-8th</td>\n",
       "      <td>4</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Transport-moving</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Amer-Indian-Eskimo</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>25</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>176756</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>32</td>\n",
       "      <td>Private</td>\n",
       "      <td>186824</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>28887</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>43</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>292175</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>193524</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>16</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>54</td>\n",
       "      <td>Private</td>\n",
       "      <td>302146</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>35</td>\n",
       "      <td>Federal-gov</td>\n",
       "      <td>76845</td>\n",
       "      <td>9th</td>\n",
       "      <td>5</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>43</td>\n",
       "      <td>Private</td>\n",
       "      <td>117037</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Transport-moving</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>2042</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>59</td>\n",
       "      <td>Private</td>\n",
       "      <td>109015</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>56</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>216851</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>19</td>\n",
       "      <td>Private</td>\n",
       "      <td>168294</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>54</td>\n",
       "      <td>?</td>\n",
       "      <td>180211</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>?</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>South</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>367260</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>49</td>\n",
       "      <td>Private</td>\n",
       "      <td>193366</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32531</th>\n",
       "      <td>30</td>\n",
       "      <td>?</td>\n",
       "      <td>33811</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32532</th>\n",
       "      <td>34</td>\n",
       "      <td>Private</td>\n",
       "      <td>204461</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>16</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32533</th>\n",
       "      <td>54</td>\n",
       "      <td>Private</td>\n",
       "      <td>337992</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>Japan</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32534</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>179137</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32535</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>325033</td>\n",
       "      <td>12th</td>\n",
       "      <td>8</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32536</th>\n",
       "      <td>34</td>\n",
       "      <td>Private</td>\n",
       "      <td>160216</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32537</th>\n",
       "      <td>30</td>\n",
       "      <td>Private</td>\n",
       "      <td>345898</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32538</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>139180</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>15020</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32539</th>\n",
       "      <td>71</td>\n",
       "      <td>?</td>\n",
       "      <td>287372</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>16</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>?</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32540</th>\n",
       "      <td>45</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>252208</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32541</th>\n",
       "      <td>41</td>\n",
       "      <td>?</td>\n",
       "      <td>202822</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Separated</td>\n",
       "      <td>?</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32542</th>\n",
       "      <td>72</td>\n",
       "      <td>?</td>\n",
       "      <td>129912</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>?</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32543</th>\n",
       "      <td>45</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>119199</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32544</th>\n",
       "      <td>31</td>\n",
       "      <td>Private</td>\n",
       "      <td>199655</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Other</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32545</th>\n",
       "      <td>39</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>111499</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32546</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>198216</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32547</th>\n",
       "      <td>43</td>\n",
       "      <td>Private</td>\n",
       "      <td>260761</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32548</th>\n",
       "      <td>65</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>99359</td>\n",
       "      <td>Prof-school</td>\n",
       "      <td>15</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>1086</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32549</th>\n",
       "      <td>43</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>255835</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32550</th>\n",
       "      <td>43</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>27242</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32551</th>\n",
       "      <td>32</td>\n",
       "      <td>Private</td>\n",
       "      <td>34066</td>\n",
       "      <td>10th</td>\n",
       "      <td>6</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Amer-Indian-Eskimo</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32552</th>\n",
       "      <td>43</td>\n",
       "      <td>Private</td>\n",
       "      <td>84661</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32553</th>\n",
       "      <td>32</td>\n",
       "      <td>Private</td>\n",
       "      <td>116138</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32554</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>321865</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32555</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>310152</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32556</th>\n",
       "      <td>27</td>\n",
       "      <td>Private</td>\n",
       "      <td>257302</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32557</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>154374</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32558</th>\n",
       "      <td>58</td>\n",
       "      <td>Private</td>\n",
       "      <td>151910</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32559</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>201490</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32560</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>287927</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32561 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age          workclass  fnlwgt      education  education-num  \\\n",
       "0       39          State-gov   77516      Bachelors             13   \n",
       "1       50   Self-emp-not-inc   83311      Bachelors             13   \n",
       "2       38            Private  215646        HS-grad              9   \n",
       "3       53            Private  234721           11th              7   \n",
       "4       28            Private  338409      Bachelors             13   \n",
       "5       37            Private  284582        Masters             14   \n",
       "6       49            Private  160187            9th              5   \n",
       "7       52   Self-emp-not-inc  209642        HS-grad              9   \n",
       "8       31            Private   45781        Masters             14   \n",
       "9       42            Private  159449      Bachelors             13   \n",
       "10      37            Private  280464   Some-college             10   \n",
       "11      30          State-gov  141297      Bachelors             13   \n",
       "12      23            Private  122272      Bachelors             13   \n",
       "13      32            Private  205019     Assoc-acdm             12   \n",
       "14      40            Private  121772      Assoc-voc             11   \n",
       "15      34            Private  245487        7th-8th              4   \n",
       "16      25   Self-emp-not-inc  176756        HS-grad              9   \n",
       "17      32            Private  186824        HS-grad              9   \n",
       "18      38            Private   28887           11th              7   \n",
       "19      43   Self-emp-not-inc  292175        Masters             14   \n",
       "20      40            Private  193524      Doctorate             16   \n",
       "21      54            Private  302146        HS-grad              9   \n",
       "22      35        Federal-gov   76845            9th              5   \n",
       "23      43            Private  117037           11th              7   \n",
       "24      59            Private  109015        HS-grad              9   \n",
       "25      56          Local-gov  216851      Bachelors             13   \n",
       "26      19            Private  168294        HS-grad              9   \n",
       "27      54                  ?  180211   Some-college             10   \n",
       "28      39            Private  367260        HS-grad              9   \n",
       "29      49            Private  193366        HS-grad              9   \n",
       "...    ...                ...     ...            ...            ...   \n",
       "32531   30                  ?   33811      Bachelors             13   \n",
       "32532   34            Private  204461      Doctorate             16   \n",
       "32533   54            Private  337992      Bachelors             13   \n",
       "32534   37            Private  179137   Some-college             10   \n",
       "32535   22            Private  325033           12th              8   \n",
       "32536   34            Private  160216      Bachelors             13   \n",
       "32537   30            Private  345898        HS-grad              9   \n",
       "32538   38            Private  139180      Bachelors             13   \n",
       "32539   71                  ?  287372      Doctorate             16   \n",
       "32540   45          State-gov  252208        HS-grad              9   \n",
       "32541   41                  ?  202822        HS-grad              9   \n",
       "32542   72                  ?  129912        HS-grad              9   \n",
       "32543   45          Local-gov  119199     Assoc-acdm             12   \n",
       "32544   31            Private  199655        Masters             14   \n",
       "32545   39          Local-gov  111499     Assoc-acdm             12   \n",
       "32546   37            Private  198216     Assoc-acdm             12   \n",
       "32547   43            Private  260761        HS-grad              9   \n",
       "32548   65   Self-emp-not-inc   99359    Prof-school             15   \n",
       "32549   43          State-gov  255835   Some-college             10   \n",
       "32550   43   Self-emp-not-inc   27242   Some-college             10   \n",
       "32551   32            Private   34066           10th              6   \n",
       "32552   43            Private   84661      Assoc-voc             11   \n",
       "32553   32            Private  116138        Masters             14   \n",
       "32554   53            Private  321865        Masters             14   \n",
       "32555   22            Private  310152   Some-college             10   \n",
       "32556   27            Private  257302     Assoc-acdm             12   \n",
       "32557   40            Private  154374        HS-grad              9   \n",
       "32558   58            Private  151910        HS-grad              9   \n",
       "32559   22            Private  201490        HS-grad              9   \n",
       "32560   52       Self-emp-inc  287927        HS-grad              9   \n",
       "\n",
       "               marital-status          occupation     relationship  \\\n",
       "0               Never-married        Adm-clerical    Not-in-family   \n",
       "1          Married-civ-spouse     Exec-managerial          Husband   \n",
       "2                    Divorced   Handlers-cleaners    Not-in-family   \n",
       "3          Married-civ-spouse   Handlers-cleaners          Husband   \n",
       "4          Married-civ-spouse      Prof-specialty             Wife   \n",
       "5          Married-civ-spouse     Exec-managerial             Wife   \n",
       "6       Married-spouse-absent       Other-service    Not-in-family   \n",
       "7          Married-civ-spouse     Exec-managerial          Husband   \n",
       "8               Never-married      Prof-specialty    Not-in-family   \n",
       "9          Married-civ-spouse     Exec-managerial          Husband   \n",
       "10         Married-civ-spouse     Exec-managerial          Husband   \n",
       "11         Married-civ-spouse      Prof-specialty          Husband   \n",
       "12              Never-married        Adm-clerical        Own-child   \n",
       "13              Never-married               Sales    Not-in-family   \n",
       "14         Married-civ-spouse        Craft-repair          Husband   \n",
       "15         Married-civ-spouse    Transport-moving          Husband   \n",
       "16              Never-married     Farming-fishing        Own-child   \n",
       "17              Never-married   Machine-op-inspct        Unmarried   \n",
       "18         Married-civ-spouse               Sales          Husband   \n",
       "19                   Divorced     Exec-managerial        Unmarried   \n",
       "20         Married-civ-spouse      Prof-specialty          Husband   \n",
       "21                  Separated       Other-service        Unmarried   \n",
       "22         Married-civ-spouse     Farming-fishing          Husband   \n",
       "23         Married-civ-spouse    Transport-moving          Husband   \n",
       "24                   Divorced        Tech-support        Unmarried   \n",
       "25         Married-civ-spouse        Tech-support          Husband   \n",
       "26              Never-married        Craft-repair        Own-child   \n",
       "27         Married-civ-spouse                   ?          Husband   \n",
       "28                   Divorced     Exec-managerial    Not-in-family   \n",
       "29         Married-civ-spouse        Craft-repair          Husband   \n",
       "...                       ...                 ...              ...   \n",
       "32531           Never-married                   ?    Not-in-family   \n",
       "32532      Married-civ-spouse      Prof-specialty          Husband   \n",
       "32533      Married-civ-spouse     Exec-managerial          Husband   \n",
       "32534                Divorced        Adm-clerical        Unmarried   \n",
       "32535           Never-married     Protective-serv        Own-child   \n",
       "32536           Never-married     Exec-managerial    Not-in-family   \n",
       "32537           Never-married        Craft-repair    Not-in-family   \n",
       "32538                Divorced      Prof-specialty        Unmarried   \n",
       "32539      Married-civ-spouse                   ?          Husband   \n",
       "32540               Separated        Adm-clerical        Own-child   \n",
       "32541               Separated                   ?    Not-in-family   \n",
       "32542      Married-civ-spouse                   ?          Husband   \n",
       "32543                Divorced      Prof-specialty        Unmarried   \n",
       "32544                Divorced       Other-service    Not-in-family   \n",
       "32545      Married-civ-spouse        Adm-clerical             Wife   \n",
       "32546                Divorced        Tech-support    Not-in-family   \n",
       "32547      Married-civ-spouse   Machine-op-inspct          Husband   \n",
       "32548           Never-married      Prof-specialty    Not-in-family   \n",
       "32549                Divorced        Adm-clerical   Other-relative   \n",
       "32550      Married-civ-spouse        Craft-repair          Husband   \n",
       "32551      Married-civ-spouse   Handlers-cleaners          Husband   \n",
       "32552      Married-civ-spouse               Sales          Husband   \n",
       "32553           Never-married        Tech-support    Not-in-family   \n",
       "32554      Married-civ-spouse     Exec-managerial          Husband   \n",
       "32555           Never-married     Protective-serv    Not-in-family   \n",
       "32556      Married-civ-spouse        Tech-support             Wife   \n",
       "32557      Married-civ-spouse   Machine-op-inspct          Husband   \n",
       "32558                 Widowed        Adm-clerical        Unmarried   \n",
       "32559           Never-married        Adm-clerical        Own-child   \n",
       "32560      Married-civ-spouse     Exec-managerial             Wife   \n",
       "\n",
       "                 ethnicity   gender  capital-gain  capital-loss  \\\n",
       "0                    White     Male          2174             0   \n",
       "1                    White     Male             0             0   \n",
       "2                    White     Male             0             0   \n",
       "3                    Black     Male             0             0   \n",
       "4                    Black   Female             0             0   \n",
       "5                    White   Female             0             0   \n",
       "6                    Black   Female             0             0   \n",
       "7                    White     Male             0             0   \n",
       "8                    White   Female         14084             0   \n",
       "9                    White     Male          5178             0   \n",
       "10                   Black     Male             0             0   \n",
       "11      Asian-Pac-Islander     Male             0             0   \n",
       "12                   White   Female             0             0   \n",
       "13                   Black     Male             0             0   \n",
       "14      Asian-Pac-Islander     Male             0             0   \n",
       "15      Amer-Indian-Eskimo     Male             0             0   \n",
       "16                   White     Male             0             0   \n",
       "17                   White     Male             0             0   \n",
       "18                   White     Male             0             0   \n",
       "19                   White   Female             0             0   \n",
       "20                   White     Male             0             0   \n",
       "21                   Black   Female             0             0   \n",
       "22                   Black     Male             0             0   \n",
       "23                   White     Male             0          2042   \n",
       "24                   White   Female             0             0   \n",
       "25                   White     Male             0             0   \n",
       "26                   White     Male             0             0   \n",
       "27      Asian-Pac-Islander     Male             0             0   \n",
       "28                   White     Male             0             0   \n",
       "29                   White     Male             0             0   \n",
       "...                    ...      ...           ...           ...   \n",
       "32531   Asian-Pac-Islander   Female             0             0   \n",
       "32532                White     Male             0             0   \n",
       "32533   Asian-Pac-Islander     Male             0             0   \n",
       "32534                White   Female             0             0   \n",
       "32535                Black     Male             0             0   \n",
       "32536                White   Female             0             0   \n",
       "32537                Black     Male             0             0   \n",
       "32538                Black   Female         15020             0   \n",
       "32539                White     Male             0             0   \n",
       "32540                White   Female             0             0   \n",
       "32541                Black   Female             0             0   \n",
       "32542                White     Male             0             0   \n",
       "32543                White   Female             0             0   \n",
       "32544                Other   Female             0             0   \n",
       "32545                White   Female             0             0   \n",
       "32546                White   Female             0             0   \n",
       "32547                White     Male             0             0   \n",
       "32548                White     Male          1086             0   \n",
       "32549                White   Female             0             0   \n",
       "32550                White     Male             0             0   \n",
       "32551   Amer-Indian-Eskimo     Male             0             0   \n",
       "32552                White     Male             0             0   \n",
       "32553   Asian-Pac-Islander     Male             0             0   \n",
       "32554                White     Male             0             0   \n",
       "32555                White     Male             0             0   \n",
       "32556                White   Female             0             0   \n",
       "32557                White     Male             0             0   \n",
       "32558                White   Female             0             0   \n",
       "32559                White     Male             0             0   \n",
       "32560                White   Female         15024             0   \n",
       "\n",
       "       hours-per-week  native-country    loan  \n",
       "0                  40   United-States   <=50K  \n",
       "1                  13   United-States   <=50K  \n",
       "2                  40   United-States   <=50K  \n",
       "3                  40   United-States   <=50K  \n",
       "4                  40            Cuba   <=50K  \n",
       "5                  40   United-States   <=50K  \n",
       "6                  16         Jamaica   <=50K  \n",
       "7                  45   United-States    >50K  \n",
       "8                  50   United-States    >50K  \n",
       "9                  40   United-States    >50K  \n",
       "10                 80   United-States    >50K  \n",
       "11                 40           India    >50K  \n",
       "12                 30   United-States   <=50K  \n",
       "13                 50   United-States   <=50K  \n",
       "14                 40               ?    >50K  \n",
       "15                 45          Mexico   <=50K  \n",
       "16                 35   United-States   <=50K  \n",
       "17                 40   United-States   <=50K  \n",
       "18                 50   United-States   <=50K  \n",
       "19                 45   United-States    >50K  \n",
       "20                 60   United-States    >50K  \n",
       "21                 20   United-States   <=50K  \n",
       "22                 40   United-States   <=50K  \n",
       "23                 40   United-States   <=50K  \n",
       "24                 40   United-States   <=50K  \n",
       "25                 40   United-States    >50K  \n",
       "26                 40   United-States   <=50K  \n",
       "27                 60           South    >50K  \n",
       "28                 80   United-States   <=50K  \n",
       "29                 40   United-States   <=50K  \n",
       "...               ...             ...     ...  \n",
       "32531              99   United-States   <=50K  \n",
       "32532              60   United-States    >50K  \n",
       "32533              50           Japan    >50K  \n",
       "32534              39   United-States   <=50K  \n",
       "32535              35   United-States   <=50K  \n",
       "32536              55   United-States    >50K  \n",
       "32537              46   United-States   <=50K  \n",
       "32538              45   United-States    >50K  \n",
       "32539              10   United-States    >50K  \n",
       "32540              40   United-States   <=50K  \n",
       "32541              32   United-States   <=50K  \n",
       "32542              25   United-States   <=50K  \n",
       "32543              48   United-States   <=50K  \n",
       "32544              30   United-States   <=50K  \n",
       "32545              20   United-States    >50K  \n",
       "32546              40   United-States   <=50K  \n",
       "32547              40          Mexico   <=50K  \n",
       "32548              60   United-States   <=50K  \n",
       "32549              40   United-States   <=50K  \n",
       "32550              50   United-States   <=50K  \n",
       "32551              40   United-States   <=50K  \n",
       "32552              45   United-States   <=50K  \n",
       "32553              11          Taiwan   <=50K  \n",
       "32554              40   United-States    >50K  \n",
       "32555              40   United-States   <=50K  \n",
       "32556              38   United-States   <=50K  \n",
       "32557              40   United-States    >50K  \n",
       "32558              40   United-States   <=50K  \n",
       "32559              20   United-States   <=50K  \n",
       "32560              40   United-States    >50K  \n",
       "\n",
       "[32561 rows x 15 columns]"
      ]
     },
     "execution_count": 555,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 670,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5ea65760f0>"
      ]
     },
     "execution_count": 670,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a4_dims = (10,5)\n",
    "fig, ax = plt.subplots(figsize=a4_dims)\n",
    "sn.violinplot(x='hours-per-week', y='gender', data=df, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 591,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/statsmodels/nonparametric/kernels.py:128: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  return (1. / np.sqrt(2 * np.pi)) * np.exp(-(Xi - x)**2 / (h**2 * 2.))\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/statsmodels/nonparametric/kernels.py:128: RuntimeWarning: invalid value encountered in true_divide\n",
      "  return (1. / np.sqrt(2 * np.pi)) * np.exp(-(Xi - x)**2 / (h**2 * 2.))\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/statsmodels/nonparametric/_kernel_base.py:513: RuntimeWarning: invalid value encountered in true_divide\n",
      "  dens = Kval.prod(axis=1) / np.prod(bw[iscontinuous])\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1557: UserWarning: Warning: converting a masked element to nan.\n",
      "  self.zmax = float(z.max())\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1558: UserWarning: Warning: converting a masked element to nan.\n",
      "  self.zmin = float(z.min())\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1203: RuntimeWarning: invalid value encountered in less\n",
      "  under = np.nonzero(lev < self.zmin)[0]\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1205: RuntimeWarning: invalid value encountered in greater\n",
      "  over = np.nonzero(lev > self.zmax)[0]\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1239: RuntimeWarning: invalid value encountered in greater\n",
      "  inside = (self.levels > self.zmin) & (self.levels < self.zmax)\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1239: RuntimeWarning: invalid value encountered in less\n",
      "  inside = (self.levels > self.zmin) & (self.levels < self.zmax)\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1243: UserWarning: No contour levels were found within the data range.\n",
      "  warnings.warn(\"No contour levels were found\"\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1557: UserWarning: Warning: converting a masked element to nan.\n",
      "  self.zmax = float(z.max())\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1558: UserWarning: Warning: converting a masked element to nan.\n",
      "  self.zmin = float(z.min())\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1243: UserWarning: No contour levels were found within the data range.\n",
      "  warnings.warn(\"No contour levels were found\"\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1557: UserWarning: Warning: converting a masked element to nan.\n",
      "  self.zmax = float(z.max())\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1558: UserWarning: Warning: converting a masked element to nan.\n",
      "  self.zmin = float(z.min())\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1243: UserWarning: No contour levels were found within the data range.\n",
      "  warnings.warn(\"No contour levels were found\"\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1557: UserWarning: Warning: converting a masked element to nan.\n",
      "  self.zmax = float(z.max())\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1558: UserWarning: Warning: converting a masked element to nan.\n",
      "  self.zmin = float(z.min())\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1243: UserWarning: No contour levels were found within the data range.\n",
      "  warnings.warn(\"No contour levels were found\"\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1557: UserWarning: Warning: converting a masked element to nan.\n",
      "  self.zmax = float(z.max())\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1558: UserWarning: Warning: converting a masked element to nan.\n",
      "  self.zmin = float(z.min())\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1243: UserWarning: No contour levels were found within the data range.\n",
      "  warnings.warn(\"No contour levels were found\"\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1557: UserWarning: Warning: converting a masked element to nan.\n",
      "  self.zmax = float(z.max())\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1558: UserWarning: Warning: converting a masked element to nan.\n",
      "  self.zmin = float(z.min())\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1243: UserWarning: No contour levels were found within the data range.\n",
      "  warnings.warn(\"No contour levels were found\"\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1557: UserWarning: Warning: converting a masked element to nan.\n",
      "  self.zmax = float(z.max())\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1558: UserWarning: Warning: converting a masked element to nan.\n",
      "  self.zmin = float(z.min())\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1243: UserWarning: No contour levels were found within the data range.\n",
      "  warnings.warn(\"No contour levels were found\"\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1557: UserWarning: Warning: converting a masked element to nan.\n",
      "  self.zmax = float(z.max())\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1558: UserWarning: Warning: converting a masked element to nan.\n",
      "  self.zmin = float(z.min())\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1243: UserWarning: No contour levels were found within the data range.\n",
      "  warnings.warn(\"No contour levels were found\"\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1557: UserWarning: Warning: converting a masked element to nan.\n",
      "  self.zmax = float(z.max())\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1558: UserWarning: Warning: converting a masked element to nan.\n",
      "  self.zmin = float(z.min())\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1243: UserWarning: No contour levels were found within the data range.\n",
      "  warnings.warn(\"No contour levels were found\"\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1557: UserWarning: Warning: converting a masked element to nan.\n",
      "  self.zmax = float(z.max())\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1558: UserWarning: Warning: converting a masked element to nan.\n",
      "  self.zmin = float(z.min())\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1243: UserWarning: No contour levels were found within the data range.\n",
      "  warnings.warn(\"No contour levels were found\"\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1557: UserWarning: Warning: converting a masked element to nan.\n",
      "  self.zmax = float(z.max())\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1558: UserWarning: Warning: converting a masked element to nan.\n",
      "  self.zmin = float(z.min())\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1243: UserWarning: No contour levels were found within the data range.\n",
      "  warnings.warn(\"No contour levels were found\"\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1557: UserWarning: Warning: converting a masked element to nan.\n",
      "  self.zmax = float(z.max())\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1558: UserWarning: Warning: converting a masked element to nan.\n",
      "  self.zmin = float(z.min())\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1243: UserWarning: No contour levels were found within the data range.\n",
      "  warnings.warn(\"No contour levels were found\"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1557: UserWarning: Warning: converting a masked element to nan.\n",
      "  self.zmax = float(z.max())\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1558: UserWarning: Warning: converting a masked element to nan.\n",
      "  self.zmin = float(z.min())\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1243: UserWarning: No contour levels were found within the data range.\n",
      "  warnings.warn(\"No contour levels were found\"\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1557: UserWarning: Warning: converting a masked element to nan.\n",
      "  self.zmax = float(z.max())\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1558: UserWarning: Warning: converting a masked element to nan.\n",
      "  self.zmin = float(z.min())\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1243: UserWarning: No contour levels were found within the data range.\n",
      "  warnings.warn(\"No contour levels were found\"\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1557: UserWarning: Warning: converting a masked element to nan.\n",
      "  self.zmax = float(z.max())\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1558: UserWarning: Warning: converting a masked element to nan.\n",
      "  self.zmin = float(z.min())\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1243: UserWarning: No contour levels were found within the data range.\n",
      "  warnings.warn(\"No contour levels were found\"\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1557: UserWarning: Warning: converting a masked element to nan.\n",
      "  self.zmax = float(z.max())\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1558: UserWarning: Warning: converting a masked element to nan.\n",
      "  self.zmin = float(z.min())\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1243: UserWarning: No contour levels were found within the data range.\n",
      "  warnings.warn(\"No contour levels were found\"\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1557: UserWarning: Warning: converting a masked element to nan.\n",
      "  self.zmax = float(z.max())\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1558: UserWarning: Warning: converting a masked element to nan.\n",
      "  self.zmin = float(z.min())\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1243: UserWarning: No contour levels were found within the data range.\n",
      "  warnings.warn(\"No contour levels were found\"\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1557: UserWarning: Warning: converting a masked element to nan.\n",
      "  self.zmax = float(z.max())\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1558: UserWarning: Warning: converting a masked element to nan.\n",
      "  self.zmin = float(z.min())\n",
      "/home/alejandro/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1243: UserWarning: No contour levels were found within the data range.\n",
      "  warnings.warn(\"No contour levels were found\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 42 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Categorical plots\n",
    "# TODO: https://seaborn.pydata.org/tutorial/categorical.html#categorical-tutorial\n",
    "\n",
    "# Numeric plots:\n",
    "# TODO: https://seaborn.pydata.org/tutorial/axis_grids.html#grid-tutorial\n",
    "\n",
    "# Statistical relationships with data\n",
    "# TODO: https://seaborn.pydata.org/tutorial/relational.html#relational-tutorial\n",
    "g = sns.PairGrid(df, hue=\"loan\")\n",
    "g.map(plt.scatter);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 674,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def kdeplot(feature):\n",
    "    plt.figure(figsize=(9, 4))\n",
    "    plt.title(\"KDE for {}\".format(feature))\n",
    "    ax0 = sns.kdeplot(df[df['gender'] == ' Male'][feature].dropna(), color= 'navy', label= 'Loan: No')\n",
    "    ax1 = sns.kdeplot(df[df['gender'] == ' Female'][feature].dropna(), color= 'orange', label= 'Loan: Yes')\n",
    "kdeplot('hours-per-week')\n",
    "# kdeplot('education-num')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 672,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' Male', ' Female'], dtype=object)"
      ]
     },
     "execution_count": 672,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xd.df[\"gender\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XMODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 745,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, mean_squared_error, roc_curve, auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 752,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12011</th>\n",
       "      <td>0.910408</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.193409</td>\n",
       "      <td>15</td>\n",
       "      <td>-0.031360</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.145917</td>\n",
       "      <td>-0.216656</td>\n",
       "      <td>-0.035429</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23599</th>\n",
       "      <td>0.910408</td>\n",
       "      <td>1</td>\n",
       "      <td>0.610447</td>\n",
       "      <td>12</td>\n",
       "      <td>1.523415</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.145917</td>\n",
       "      <td>-0.216656</td>\n",
       "      <td>0.774456</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23603</th>\n",
       "      <td>-1.288936</td>\n",
       "      <td>4</td>\n",
       "      <td>0.119324</td>\n",
       "      <td>8</td>\n",
       "      <td>0.357334</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.145917</td>\n",
       "      <td>-0.216656</td>\n",
       "      <td>-0.035429</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6163</th>\n",
       "      <td>-0.995690</td>\n",
       "      <td>4</td>\n",
       "      <td>1.908230</td>\n",
       "      <td>15</td>\n",
       "      <td>-0.031360</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.145917</td>\n",
       "      <td>-0.216656</td>\n",
       "      <td>-1.331245</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14883</th>\n",
       "      <td>0.690473</td>\n",
       "      <td>4</td>\n",
       "      <td>0.037865</td>\n",
       "      <td>9</td>\n",
       "      <td>1.134721</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.145917</td>\n",
       "      <td>-0.216656</td>\n",
       "      <td>-0.197406</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            age  workclass    fnlwgt  education  education-num  \\\n",
       "12011  0.910408          4 -0.193409         15      -0.031360   \n",
       "23599  0.910408          1  0.610447         12       1.523415   \n",
       "23603 -1.288936          4  0.119324          8       0.357334   \n",
       "6163  -0.995690          4  1.908230         15      -0.031360   \n",
       "14883  0.690473          4  0.037865          9       1.134721   \n",
       "\n",
       "       marital-status  occupation  relationship  ethnicity  gender  \\\n",
       "12011               0           6             1          4       0   \n",
       "23599               6          12             4          4       1   \n",
       "23603               4           3             3          2       1   \n",
       "6163                4          12             3          4       1   \n",
       "14883               0           1             3          4       1   \n",
       "\n",
       "       capital-gain  capital-loss  hours-per-week  native-country  \n",
       "12011     -0.145917     -0.216656       -0.035429              21  \n",
       "23599     -0.145917     -0.216656        0.774456               8  \n",
       "23603     -0.145917     -0.216656       -0.035429              39  \n",
       "6163      -0.145917     -0.216656       -1.331245              39  \n",
       "14883     -0.145917     -0.216656       -0.197406              39  "
      ]
     },
     "execution_count": 752,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xd.reset()\n",
    "_= xd.normalize_numeric()\n",
    "_= xd.convert_categories()\n",
    "\n",
    "X = xd.df.drop(xd._target_name, axis=1).copy()\n",
    "y = xd.df[xd._target_name].astype(int).values.copy()\n",
    "\n",
    "X_train, X_valid, y_train, y_valid = \\\n",
    "        train_test_split(X, y, test_size=0.2, random_state=7)\n",
    "\n",
    "X_train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 754,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 26048 samples, validate on 6513 samples\n",
      "Epoch 1/50\n",
      "26048/26048 [==============================] - 1s 22us/step - loss: 0.5790 - acc: 0.7212 - val_loss: 0.4651 - val_acc: 0.7981\n",
      "Epoch 2/50\n",
      "26048/26048 [==============================] - 0s 4us/step - loss: 0.4410 - acc: 0.8051 - val_loss: 0.3969 - val_acc: 0.8219\n",
      "Epoch 3/50\n",
      "26048/26048 [==============================] - 0s 5us/step - loss: 0.3880 - acc: 0.8224 - val_loss: 0.3536 - val_acc: 0.8363\n",
      "Epoch 4/50\n",
      "26048/26048 [==============================] - 0s 5us/step - loss: 0.3492 - acc: 0.8401 - val_loss: 0.3288 - val_acc: 0.8452\n",
      "Epoch 5/50\n",
      "26048/26048 [==============================] - 0s 4us/step - loss: 0.3338 - acc: 0.8445 - val_loss: 0.3221 - val_acc: 0.8475\n",
      "Epoch 6/50\n",
      "26048/26048 [==============================] - 0s 5us/step - loss: 0.3282 - acc: 0.8478 - val_loss: 0.3189 - val_acc: 0.8486\n",
      "Epoch 7/50\n",
      "26048/26048 [==============================] - 0s 5us/step - loss: 0.3243 - acc: 0.8495 - val_loss: 0.3171 - val_acc: 0.8511\n",
      "Epoch 8/50\n",
      "26048/26048 [==============================] - 0s 5us/step - loss: 0.3240 - acc: 0.8498 - val_loss: 0.3161 - val_acc: 0.8534\n",
      "Epoch 9/50\n",
      "26048/26048 [==============================] - 0s 5us/step - loss: 0.3194 - acc: 0.8512 - val_loss: 0.3151 - val_acc: 0.8523\n",
      "Epoch 10/50\n",
      "26048/26048 [==============================] - 0s 5us/step - loss: 0.3175 - acc: 0.8519 - val_loss: 0.3147 - val_acc: 0.8532\n",
      "Epoch 11/50\n",
      "26048/26048 [==============================] - 0s 4us/step - loss: 0.3172 - acc: 0.8547 - val_loss: 0.3144 - val_acc: 0.8537\n",
      "Epoch 12/50\n",
      "26048/26048 [==============================] - 0s 4us/step - loss: 0.3159 - acc: 0.8540 - val_loss: 0.3137 - val_acc: 0.8532\n",
      "Epoch 13/50\n",
      "26048/26048 [==============================] - 0s 4us/step - loss: 0.3135 - acc: 0.8556 - val_loss: 0.3133 - val_acc: 0.8534\n",
      "Epoch 14/50\n",
      "26048/26048 [==============================] - 0s 4us/step - loss: 0.3141 - acc: 0.8567 - val_loss: 0.3130 - val_acc: 0.8538\n",
      "Epoch 15/50\n",
      "26048/26048 [==============================] - 0s 4us/step - loss: 0.3146 - acc: 0.8543 - val_loss: 0.3128 - val_acc: 0.8543\n",
      "Epoch 16/50\n",
      "26048/26048 [==============================] - 0s 5us/step - loss: 0.3134 - acc: 0.8554 - val_loss: 0.3127 - val_acc: 0.8549\n",
      "Epoch 17/50\n",
      "26048/26048 [==============================] - 0s 4us/step - loss: 0.3118 - acc: 0.8553 - val_loss: 0.3126 - val_acc: 0.8544\n",
      "Epoch 18/50\n",
      "26048/26048 [==============================] - 0s 4us/step - loss: 0.3127 - acc: 0.8554 - val_loss: 0.3124 - val_acc: 0.8549\n",
      "Epoch 19/50\n",
      "26048/26048 [==============================] - 0s 5us/step - loss: 0.3121 - acc: 0.8557 - val_loss: 0.3126 - val_acc: 0.8548\n",
      "Epoch 20/50\n",
      "26048/26048 [==============================] - 0s 4us/step - loss: 0.3119 - acc: 0.8570 - val_loss: 0.3131 - val_acc: 0.8551\n",
      "Epoch 21/50\n",
      "26048/26048 [==============================] - 0s 5us/step - loss: 0.3126 - acc: 0.8564 - val_loss: 0.3123 - val_acc: 0.8546\n",
      "Epoch 22/50\n",
      "26048/26048 [==============================] - 0s 4us/step - loss: 0.3106 - acc: 0.8570 - val_loss: 0.3121 - val_acc: 0.8551\n",
      "Epoch 23/50\n",
      "26048/26048 [==============================] - 0s 4us/step - loss: 0.3099 - acc: 0.8566 - val_loss: 0.3121 - val_acc: 0.8551\n",
      "Epoch 24/50\n",
      "26048/26048 [==============================] - 0s 4us/step - loss: 0.3101 - acc: 0.8573 - val_loss: 0.3119 - val_acc: 0.8552\n",
      "Epoch 25/50\n",
      "26048/26048 [==============================] - 0s 4us/step - loss: 0.3093 - acc: 0.8580 - val_loss: 0.3124 - val_acc: 0.8554\n",
      "Epoch 26/50\n",
      "26048/26048 [==============================] - 0s 4us/step - loss: 0.3093 - acc: 0.8568 - val_loss: 0.3120 - val_acc: 0.8548\n",
      "Epoch 27/50\n",
      "26048/26048 [==============================] - 0s 5us/step - loss: 0.3085 - acc: 0.8578 - val_loss: 0.3121 - val_acc: 0.8551\n",
      "Epoch 28/50\n",
      "26048/26048 [==============================] - 0s 4us/step - loss: 0.3098 - acc: 0.8585 - val_loss: 0.3122 - val_acc: 0.8548\n",
      "Epoch 29/50\n",
      "26048/26048 [==============================] - 0s 4us/step - loss: 0.3092 - acc: 0.8600 - val_loss: 0.3122 - val_acc: 0.8555\n",
      "Epoch 30/50\n",
      "26048/26048 [==============================] - 0s 4us/step - loss: 0.3078 - acc: 0.8576 - val_loss: 0.3124 - val_acc: 0.8557\n",
      "Epoch 31/50\n",
      "26048/26048 [==============================] - 0s 4us/step - loss: 0.3093 - acc: 0.8578 - val_loss: 0.3122 - val_acc: 0.8560\n",
      "Epoch 32/50\n",
      "26048/26048 [==============================] - 0s 4us/step - loss: 0.3092 - acc: 0.8572 - val_loss: 0.3121 - val_acc: 0.8560\n",
      "Epoch 33/50\n",
      "26048/26048 [==============================] - 0s 4us/step - loss: 0.3085 - acc: 0.8582 - val_loss: 0.3122 - val_acc: 0.8555\n",
      "Epoch 34/50\n",
      "26048/26048 [==============================] - 0s 4us/step - loss: 0.3069 - acc: 0.8593 - val_loss: 0.3123 - val_acc: 0.8558\n",
      "Epoch 35/50\n",
      "26048/26048 [==============================] - 0s 4us/step - loss: 0.3076 - acc: 0.8587 - val_loss: 0.3121 - val_acc: 0.8569\n",
      "Epoch 36/50\n",
      "26048/26048 [==============================] - 0s 4us/step - loss: 0.3071 - acc: 0.8579 - val_loss: 0.3124 - val_acc: 0.8563\n",
      "Epoch 37/50\n",
      "26048/26048 [==============================] - 0s 4us/step - loss: 0.3069 - acc: 0.8589 - val_loss: 0.3129 - val_acc: 0.8560\n",
      "Epoch 38/50\n",
      "26048/26048 [==============================] - 0s 4us/step - loss: 0.3083 - acc: 0.8590 - val_loss: 0.3122 - val_acc: 0.8558\n",
      "Epoch 39/50\n",
      "26048/26048 [==============================] - 0s 4us/step - loss: 0.3066 - acc: 0.8591 - val_loss: 0.3122 - val_acc: 0.8561\n",
      "Epoch 40/50\n",
      "26048/26048 [==============================] - 0s 4us/step - loss: 0.3065 - acc: 0.8593 - val_loss: 0.3126 - val_acc: 0.8557\n",
      "Epoch 41/50\n",
      "26048/26048 [==============================] - 0s 4us/step - loss: 0.3067 - acc: 0.8591 - val_loss: 0.3125 - val_acc: 0.8557\n",
      "Epoch 42/50\n",
      "26048/26048 [==============================] - 0s 5us/step - loss: 0.3076 - acc: 0.8590 - val_loss: 0.3128 - val_acc: 0.8552\n",
      "Epoch 43/50\n",
      "26048/26048 [==============================] - 0s 4us/step - loss: 0.3058 - acc: 0.8584 - val_loss: 0.3126 - val_acc: 0.8555\n",
      "Epoch 44/50\n",
      "26048/26048 [==============================] - 0s 4us/step - loss: 0.3059 - acc: 0.8593 - val_loss: 0.3124 - val_acc: 0.8560\n",
      "Epoch 45/50\n",
      "26048/26048 [==============================] - 0s 5us/step - loss: 0.3063 - acc: 0.8604 - val_loss: 0.3122 - val_acc: 0.8558\n",
      "Epoch 46/50\n",
      "26048/26048 [==============================] - 0s 5us/step - loss: 0.3073 - acc: 0.8597 - val_loss: 0.3125 - val_acc: 0.8548\n",
      "Epoch 47/50\n",
      "26048/26048 [==============================] - 0s 4us/step - loss: 0.3061 - acc: 0.8601 - val_loss: 0.3126 - val_acc: 0.8561\n",
      "Epoch 48/50\n",
      "26048/26048 [==============================] - 0s 4us/step - loss: 0.3072 - acc: 0.8585 - val_loss: 0.3124 - val_acc: 0.8558\n",
      "Epoch 49/50\n",
      "26048/26048 [==============================] - 0s 4us/step - loss: 0.3064 - acc: 0.8587 - val_loss: 0.3126 - val_acc: 0.8571\n",
      "Epoch 50/50\n",
      "26048/26048 [==============================] - 0s 4us/step - loss: 0.3054 - acc: 0.8597 - val_loss: 0.3122 - val_acc: 0.8567\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f5ebbefbb38>"
      ]
     },
     "execution_count": 754,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from keras.layers import Input, Dense, Flatten, \\\n",
    "    Concatenate, concatenate, Dropout, Lambda\n",
    "from keras.models import Model, Sequential\n",
    "from keras.layers.embeddings import Embedding\n",
    "\n",
    "def build_model(X):\n",
    "    input_els = []\n",
    "    encoded_els = []\n",
    "    dtypes = list(zip(X.dtypes.index, map(str, X.dtypes)))\n",
    "    for k,dtype in dtypes:\n",
    "        input_els.append(Input(shape=(1,)))\n",
    "        if dtype == \"int8\":\n",
    "            e = Flatten()(Embedding(df[k].max()+1, 1)(input_els[-1]))\n",
    "        else:\n",
    "            e = input_els[-1]\n",
    "        encoded_els.append(e)\n",
    "    encoded_els = concatenate(encoded_els)\n",
    "\n",
    "    layer1 = Dropout(0.5)(Dense(100, activation=\"relu\")(encoded_els))\n",
    "    out = Dense(1, activation='sigmoid')(layer1)\n",
    "\n",
    "    # train model\n",
    "    model = Model(inputs=input_els, outputs=[out])\n",
    "    model.compile(optimizer=\"adam\", loss='binary_crossentropy', metrics=['accuracy'])\n",
    "    return model\n",
    "\n",
    "\n",
    "def f_in(X, m=None):\n",
    "    \"\"\"Preprocess input so it can be provided to a function\"\"\"\n",
    "    if m:\n",
    "        return [X.iloc[:m,i] for i in range(X.shape[1])]\n",
    "    else:\n",
    "        return [X.iloc[:,i] for i in range(X.shape[1])]\n",
    "\n",
    "def f_out(probs):\n",
    "    \"\"\"Convert probabilities into classes\"\"\"\n",
    "    return list((probs >= 0.5).astype(int).T[0])\n",
    "\n",
    "model = build_model(X)\n",
    "\n",
    "model.fit(f_in(X_train), y_train, epochs=50,\n",
    "    batch_size=512, shuffle=True, validation_data=(f_in(X_valid), y_valid),\n",
    "    verbose=1, validation_split=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 761,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26048/26048 [==============================] - 0s 13us/step\n",
      "Error 0.3000: \n",
      "Accuracy 86.1448: \n"
     ]
    }
   ],
   "source": [
    "\n",
    "score = model.evaluate(f_in(X_train), y_train, verbose=1)\n",
    "\n",
    "probabilities = model.predict(f_in(X_valid))\n",
    "pred = f_out(probabilities)\n",
    "\n",
    "print(\"Error %.4f: \" % score[0])\n",
    "print(\"Accuracy %.4f: \" % (score[1]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 762,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5ea601ec50>"
      ]
     },
     "execution_count": 762,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion = sklearn.metrics.confusion_matrix(y_valid, pred)\n",
    "confusion_scaled = confusion.astype(\"float\") / confusion.sum(axis=1)[:, np.newaxis]\n",
    "confusion_scaled_df = pd.DataFrame(confusion_scaled, index=[\"Denied\", \"Approved\"], columns=[\"Denied\", \"Approved\"])\n",
    "sn.heatmap(confusion_scaled_df, annot=True, fmt='.2f', center=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 804,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Denied</th>\n",
       "      <th>Approved</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Denied</th>\n",
       "      <td>4598</td>\n",
       "      <td>353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Approved</th>\n",
       "      <td>580</td>\n",
       "      <td>982</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Denied  Approved\n",
       "Denied      4598       353\n",
       "Approved     580       982"
      ]
     },
     "execution_count": 804,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itertools import product\n",
    "import numpy as np\n",
    "\n",
    "def confusion_matrix(y_target, y_predicted, scale=True, plot=True):\n",
    "    confusion = sklearn.metrics.confusion_matrix(y_valid, pred)\n",
    "    if scale:\n",
    "        confusion = confusion.astype(\"float\") / confusion.sum(axis=1)[:, np.newaxis]\n",
    "    confusion_df = pd.DataFrame(confusion, index=[\"Denied\", \"Approved\"], columns=[\"Denied\", \"Approved\"])\n",
    "    if plot:\n",
    "        cm = sns.cubehelix_palette(8, start=2, rot=0, dark=0, light=1, reverse=True, as_cmap=True)\n",
    "        sn.heatmap(confusion_df, annot=True, fmt='.2f', center=1, cmap=cm)\n",
    "    return confusion_df\n",
    "\n",
    "confusion_matrix(y_valid, pred)\n",
    "plt.show()\n",
    "confusion_matrix(y_valid, pred, scale=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 806,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.93      0.91      4951\n",
      "           1       0.74      0.63      0.68      1562\n",
      "\n",
      "   micro avg       0.86      0.86      0.86      6513\n",
      "   macro avg       0.81      0.78      0.79      6513\n",
      "weighted avg       0.85      0.86      0.85      6513\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(y_valid, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 826,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([19769,  6279])"
      ]
     },
     "execution_count": 826,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.histogram(y_train,bins=2)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
